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Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.
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Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.
Low-mass X-ray binaries (LMXBs) show evidence of a global correlation of debated origin between X-ray and optical luminosity. We study for the first time this correlation in two transient LMXBs, the black hole (BH) V404 Cyg and the neutron star Cen X-4, over six orders of magnitude in X-ray luminosity, from outburst to quiescence. After subtracting the contribution from the companion star, the Cen X-4 data can be described by a single power-law correlation of the form L{sub opt}∝ L{sub X}{sup 0.44}, consistent with disk reprocessing. We find a similar correlation slope for V404 Cyg in quiescence (0.46) and a steeper one (0.56) in the outburst hard state of 1989. However, V404 Cyg is about 160–280 times optically brighter, at a given 3–9 keV X-ray luminosity, compared to Cen X-4. This ratio is a factor of 10 smaller in quiescence, where the normalization of the V404 Cyg correlation also changes. Once the bolometric X-ray emission is considered and the known main differences between V404 Cyg and Cen X-4 are taken into account (a larger compact object mass, accretion disk size, and the presence of a strong jet contribution in the hard state for the BH system), the two systems lie on the same correlation. In V404 Cyg, the jet dominates spectrally at optical–infrared frequencies during the hard state but makes a negligible contribution in quiescence, which may account for the change in its correlation slope and normalization. These results provide a benchmark to compare with data from the 2015 outburst of V404 Cyg and, potentially, other transient LMXBs as well.
Leisingera methylohalidivorans Schaefer et al. 2002 emend. Vandecandelaere et al. 2008 is the type species of the genus Leisingera. The genus belongs to the Roseobacter clade (Rhodobacteraceae, Alphaproteobacteria), a widely distributed lineage in marine environments. Leisingera and particularly L. methylohalidivorans strain MB2T is of special interest due to its methylotrophy. Here we describe the complete genome sequence and annotation of this bacterium together with previously unreported aspects of its phenotype. The 4,650,996 bp long genome with its 4,515 protein-coding and 81 RNA genes consists of three replicons, a single chromosome and two extrachromosomal elements with sizes of 221 kb and 285 kb.
Strain T5 T is the type strain of the species Phaeobacter inhibens Martens et al. 2006, a secondary metabolite producing bacterium affiliated to the Roseobacter clade. Strain T5 T was isolated from a water sample taken at the German Wadden Sea, southern North Sea. Here we describe the complete genome sequence and annotation of this bacterium with a special focus on the secondary metabolism and compare it with the genomes of the Phaeobacter inhibens strains DSM 17395 and DSM 24588 (2.10), selected because of the close phylogenetic relationship based on the 16S rRNA gene sequences of these three strains. The genome of strain T5 T comprises 4,130,897 bp with 3.923 protein-coding genes and shows high similarities in genetic and genomic characteristics compared to P. inhibens DSM 17395 and DSM 24588 (2.10). Besides the chromosome, strain T5 T possesses four plasmids, three of which show a high similarity to the plasmids of the strains DSM 17395 and DSM 24588 (2.10). Analysis of the fourth plasmid suggested horizontal gene transfer. Most of the genes on this plasmid are not present in the strains DSM 17395 and DSM 24588 (2.10) including a nitrous oxide reductase, which allows strain T5 T a facultative anaerobic lifestyle. The G+C content was calculated from the genome sequence and differs significantly from the previously published value, thus warranting an emendation of the species description.
The compatibilities of fuel system elastomers and plastics were evaluated for test fuels containing 16 vol.% isobutanol (iBu16) and 10 vol.% ethanol (E10). Elastomers included two fluorocarbons, four acrylonitrile butadiene rubbers (NBRs), and one type of fluorosilicone, neoprene, and epichlorohydrin/ethylene oxide. Plastic materials included four nylon grades, three polyamides, polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF), ethylene tetrafluoroethylene (ETFE), polyphenylene sulfide (PPS), high-density polyethylene (HDPE), polybutylene terephthalate (PBT), polyoxymethylene (POM), flexible polyvinylchloride (PVC), polyetherimide (PEI), polyetheretherketone (PEEK), and a phenol formaldehyde reinforced with glass fiber (GFPF). For each polymer material, the volume, mass, and hardness were measured before and after drying. Dynamic mechanical analysis (DMA) measurements were also performed on the dried specimens.For the elastomer materials the measured properties were similar for both fuels. The fluorocarbons and fluorosilicone swelled the least (~20%), while more moderate (20-45%) expansion occurred for the two NBR hose grades and (ECO). HNBR, neoprene, and silicone exhibited high swelling and softening, which likely precludes their use in many fuel systems. For the plastic materials, the observed swell was low; Nylon 11 swelled around 15%, but otherwise, their measured swell was <10%. Many of the plastics also showed sensitivity to alcohol type, as the E10 test fuel often imparted appreciably higher swell than iBu16. In general, the plastic materials showed good compatibility with the iBu16 and E10 test fuels. Here, the sole exception was the PVC material, which was structurally degraded from exposure to either fuel type. Compositional analysis showed high fuel retention in Nylon 12 and PVC. PVC also experienced a significant reduction in plasticizer compounds following exposure, which resulted in embrittlement and an increase in the glass-to-rubber transition temperature.
In collaboration with the U.S. Environmental Protection Agency and the U.S. Department of Energy, the National Renewable Energy Laboratory has conducted a national analysis of road grade characteristics experienced by U.S. medium- and heavy-duty trucks on controlled access highways. These characteristics have been developed using TomTom's commercially available street map and road grade database. Using the TomTom national road grade database, national statistics on road grade and hill distances were generated for the U.S. network of controlled access highways. These statistical distributions were then weighted using data provided by the U.S. Environmental Protection Agency for activity of medium- and heavy-duty trucks on controlled access highways. Here, the national activity-weighted road grade and hill distance distributions were then used as targets for development of a handful of sample grade profiles potentially to be used in the U.S. Environmental Protection Agency's Greenhouse Gas Emissions Model certification tool as well as in dynamometer testing of medium- and heavy-duty vehicles and their powertrains.
Evidence is mounting that the majority of the climatically active aerosols are produced through the growth of smaller particles via secondary organic aerosol (SOA) formation from gas-to-particle conversion of anthropogenic and biogenic volatile organic compounds (VOCs). The timescale of SOA partitioning and the associated size distribution dynamics are expected to depend on the gas-phase oxidation of the precursor VOCs and their products, volatility of these organic solutes, composition and phase state of the pre-existing particles, and diffusivity and reactivity of the solute within the particle phase. This paper describes a new framework for modeling kinetic gas-particle partitioning of SOA, with an analytical treatment for the diffusion-reaction process within the particle phase. The formulation is amenable for eventual use in regional and global climate models, although it currently awaits implementation of the actual particle-phase reactions that are important for SOA formation. In the present work, the model is applied to investigate the competitive growth dynamics of the Aitken and accumulation mode particles while the Kelvin effect and coagulation are neglected for simplicity. The timescale of SOA partitioning and evolution of number and composition size distributions are evaluated for a range of solute volatilities (C*), particle-phase bulk diffusivities (Db), and particle-phase reactivity, as exemplified by a pseudo-first-order rate constant (kc). Results show that irreversible condensation of non-volatile organic vapors (equivalent to ) produces significant narrowing of the size distribution. At the other extreme, non-reactive partitioning of semi-volatile organic vapors is volume-controlled in which the final (equilibrium) size distribution simply shifts to the right on the diameter axis while its shape remains unchanged. However, appreciable narrowing of the size distribution may occur when the pre-existing particles are highly viscous semi-solids such that small particles reach quasi-equilibrium much faster than the large ones. In the case of reactive partitioning (finite ), the size distribution experiences permanent narrowing, which is especially pronounced for Db < 10-13 cm2 s-1 and kc > 0.01 s-1. As a result, both number and composition size distributions are needed to effectively constrain and evaluate the next generation of SOA models that treat phase state thermodynamics, particle-phase diffusion and particle-phase chemical reactions.