Nonresidential Building Energy Consumption Survey (NBECS)

Imputation procedures were designed for the 1983 Nonresidential Buildings Energy Consumption Survey (NBECS) of the Energy Information Administration (EIA) using 1979 NBECS data. The study included methodology development, data analysis, regression analyses, empirical evaluations of the regression models, and imputation procedures. Models considered were engineering models, stepwise regression, weighted regression, nonlinear regression, and log transformation regression. A method for determining the appropriateness of the imputation model for a particular set of independent variables is recommended. Although this study was completed in 1985, this final version of the report is being issued due to continuing requests for information. 32 tabs

Conserved synteny at the protein family level reveals genes underlying Shewanella species’ cold tolerance and predicts their novel phenotypes

© The Authors 2009. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License. The definitive version was published in Functional & Integrative Genomics 10 (2010): 97-110, doi:10.1007/s10142-009-0142-y.Bacteria of the genus Shewanella can thrive in different environments and demonstrate significant variability in their metabolic and ecophysiological capabilities including cold and salt tolerance. Genomic characteristics underlying this variability across species are largely unknown. In this study, we address the problem by a comparison of the physiological, metabolic, and genomic characteristics of 19 sequenced Shewanella species. We have employed two novel approaches based on association of a phenotypic trait with the number of the trait-specific protein families (Pfam domains) and on the conservation of synteny (order in the genome) of the trait-related genes. Our first approach is top-down and involves experimental evaluation and quantification of the species’ cold tolerance followed by identification of the correlated Pfam domains and genes with a conserved synteny. The second, a bottom-up approach, predicts novel phenotypes of the species by calculating profiles of each Pfam domain among their genomes and following pair-wise correlation of the profiles and their network clustering. Using the first approach, we find a link between cold and salt tolerance of the species and the presence in the genome of a Na+/H+ antiporter gene cluster. Other cold-tolerance-related genes include peptidases, chemotaxis sensory transducer proteins, a cysteine exporter, and helicases. Using the bottom-up approach, we found several novel phenotypes in the newly sequenced Shewanella species, including degradation of aromatic compounds by an aerobic hybrid pathway in Shewanella woodyi, degradation of ethanolamine by Shewanella benthica, and propanediol degradation by Shewanella putrefaciens CN32 and Shewanella sp. W3-18-1.This research was supported by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research under the Genomics: GTL Program via the Shewanella Federation consortium

Possible Savings Achievable by Recipients of Training and Software Provided by the U.S Department of Energy’s Industrial Technologies Program

Through its Save Energy Now (SEN) Initiative, the U.S. Department of Energy’s (DOE’s) Industrial Technologies Program (ITP) disseminates information on energy efficient technologies and practices to U.S. industrial firms to improve the energy efficiency of their operations. Among other things, Save Energy Now conducts training sessions on a variety of energy systems that are important to industry (i.e., compressed air, steam, process heat, pumps, motors, and fans) and distributes software tools on those same topics. A recent Oak Ridge National Laboratory (ORNL) study collected information from recipients of SEN training and software regarding how much their total annual plant energy costs could be reduced by implementing the measures that they identified since receiving SEN services. Those same individuals were also queried regarding the portion of potential savings that were actually achieved. The responses revealed both similarities and differences between training and software recipients as well as substantial variation in the savings associated with the diverse energy systems addressed

The Global Historical Climatology Network: A preview of Version 2

Instruments that could reliably measure temperature, precipitation, and pressure were developed by the late 17th and early 18th centuries. It has been estimated that weather records have been collected at one to two hundred thousand locations since those first instruments were placed in the field. Numerous applications, from global change studies to climate impact assessments to general circulation models, make use of such historical records. Given their importance, it is unfortunate that one cannot approach a single researcher or data center to acquire all of the records for all of the stations, or even a large portion of them. In 1990, the Carbon Dioxide Information Analysis Center (CDIAC), the National Climatic Data Center (NCDC), and the World Meteorological Organization (WMO) undertook a collaborative effort aimed at solving this problem. The initiative completed its first data product, known as the Global Historical Climatology Network (GHCN) version 1.0, in 1992. This data base contains quality-controlled monthly climatic time series from 6,039 temperature, 7,533 precipitation, 1,883 sea level pressure, and 1,873 station pressure stations located on global land areas. This paper describes the data and methods being used to compile GHCN version 2.0, an expanded and improved version of its predecessor. Planned for distribution in early 1995, its enhancements will include (1) data for additional stations--perhaps three times as many as in version 1.0, plus maximum/minimum temperature measurements; (2) detailed assessments of data quality, including nearest-neighbor checks; and (3) adjustments for nonclimatic inhomogeneities, such as station relocations and land use changes

Feasibility of developing a portable driver performance data acquisition system for human factors research: Technical tasks. Volume 1

A two-phase, multi-year research program entitled ``development of a portable driver performance data acquisition system for human factors research`` was recently completed. The primary objective of the project was to develop a portable data acquisition system for crash avoidance research (DASCAR) that will allow drive performance data to be collected using a large variety of vehicle types and that would be capable of being installed on a given vehicle type within a relatively short-time frame. During phase 1 a feasibility study for designing and fabricating DASCAR was conducted. In phase 2 of the research DASCAR was actually developed and validated. This technical memorandum documents the results from the feasibility study. It is subdivided into three volumes. Volume one (this report) addresses the last five items in the phase 1 research and the first issue in the second phase of the project. Volumes two and three present the related appendices, and the design specifications developed for DASCAR respectively. The six tasks were oriented toward: identifying parameters and measures; identifying analysis tools and methods; identifying measurement techniques and state-of-the-art hardware and software; developing design requirements and specifications; determining the cost of one or more copies of the proposed data acquisition system; and designing a development plan and constructing DASCAR. This report also covers: the background to the program; the requirements for the project; micro camera testing; heat load calculations for the DASCAR instrumentation package in automobile trunks; phase 2 of the research; the DASCAR hardware and software delivered to the National Highway Traffic Safety Administration; and crash avoidance problems that can be addressed by DASCAR

Evaluation of metal and radionuclide data from neutron activation and acid-digestion-based spectrometry analyses of background soils: Significance in environmental restoration

A faster, more cost-effective, and higher-quality data acquisition procedure for natural background-level metals and radionuclides in soils is needed for remedial investigations of contaminated sites. In this project, a total of 120 soil samples were collected from uncontaminated areas on and near the Oak Ridge Reservation. The samples were taken at three different depths and from three different geologic groups to establish background concentrations of metals and radionuclides. The objective of this presentation is to discuss the advantages and disadvantages of neutron activation analysis (NAA) compared with those of acid-digestion-based spectrometry (ADS) methods; the advantages and disadvantages were evaluated from Al, Sb, As, Cr, Co, Fe, Mg, Mn, Hg, K, Ag, {sup 232}Th, {sup 235}U, {sup 238}U, V, and Zn data. The ADS methods used for this project were inductively coupled plasma (ICP), ICP-mass spectrometry (ICP-MS), and alpha spectrometry. The scatter plots showed that the NAA results for As, Co, Fe, Mn, {sup 232}Th, and {sup 238}U are reasonably correlated with the results from the other analytical methods. Compared to NAA, however, the ADS methods underestimated Al, Cr, Mg, K, V, and Zn. The skew distributions were caused by incomplete dissolution of the analytes during acid digestion of the soil samples. Because of the high detection limits of the spectrometric methods, the NAA results and the ADS results for some elements, including Sb, Hg, and Ag, did not show a definite relationship. The NAA results were highly correlated with the alpha spectrometry results for {sup 232}Th and {sup 238}U but poorly correlated for {sup 235}U, probably because of a larger counting error associated with the lower activity of the isotope. The NAA methods, including the delayed neutron counting method, were far superior techniques for quantifying background levels of radionuclides ({sup 232}Th, {sup 235}U, and {sup 238}U) and metals (Al, Cr, Mg, K, V, and Zn) in soils

Mining Credit Card Data for Decision Support (Extended Abstract)

June M. Donato, Jack C. Schryver, Nancy W. Grady, Gregory C. Hinkel, Richard L. Schmoyer Jr., Michael R. Leuze Oak Ridge National Laboratory Oak Ridge, Tennessee 37831 USA For more information contact: [email protected] 1 Introduction While it is widely recognized that data can be a valuable resource for any organization, extracting information contained within the data is often a difficult problem. Attempts to obtain information from data may be limited by legacy data storage formats, lack of expert knowledge about the data, difficulty in viewing the data, or the volume of data needing to be processed. The rapidly developing field of Data Mining or Knowledge Data Discovery is a blending of Artificial Intelligence, Statistics, and HumanComputer Interaction. Sophisticated data navigation tools to obtain the information needed for decision support do not yet exist. Each data mining task typically requires a custom solution that depends upon the character and quantity of the data. A probl..