11 research outputs found

    Study of e+eppˉe^+e^- \rightarrow p\bar{p} in the vicinity of ψ(3770)\psi(3770)

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    Using 2917 pb1\rm{pb}^{-1} of data accumulated at 3.773~GeV\rm{GeV}, 44.5~pb1\rm{pb}^{-1} of data accumulated at 3.65~GeV\rm{GeV} and data accumulated during a ψ(3770)\psi(3770) line-shape scan with the BESIII detector, the reaction e+eppˉe^+e^-\rightarrow p\bar{p} is studied considering a possible interference between resonant and continuum amplitudes. The cross section of e+eψ(3770)ppˉe^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}, σ(e+eψ(3770)ppˉ)\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}), is found to have two solutions, determined to be (0.059±0.032±0.0120.059\pm0.032\pm0.012) pb with the phase angle ϕ=(255.8±37.9±4.8)\phi = (255.8\pm37.9\pm4.8)^\circ (<<0.11 pb at the 90% confidence level), or σ(e+eψ(3770)ppˉ)=(2.57±0.12±0.12\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}) = (2.57\pm0.12\pm0.12) pb with ϕ=(266.9±6.1±0.9)\phi = (266.9\pm6.1\pm0.9)^\circ both of which agree with a destructive interference. Using the obtained cross section of ψ(3770)ppˉ\psi(3770)\rightarrow p\bar{p}, the cross section of ppˉψ(3770)p\bar{p}\rightarrow \psi(3770), which is useful information for the future PANDA experiment, is estimated to be either (9.8±5.79.8\pm5.7) nb (<17.2<17.2 nb at 90% C.L.) or (425.6±42.9)(425.6\pm42.9) nb

    Energy-saving potential prediction models for large-scale building: A state-of-the-art review

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    Energy-saving potential prediction models play a major role in developing retrofit scheme. Reliable estimation and quantification of energy saving of retrofit measures for these models is essential, since it is often used for guiding political decision-makers. The aim of this paper is to provide up-to-date approaches of predicting energy-saving effect for building retrofit in large-scale, including data-driven, physics-based, and hybrid approaches, while throwing light on workflow and key factors in developing models. The review focuses on pointing out pivotal aspects that are not considered in current models of predicting energy-saving effect for building retrofit in large-scale. It is concluded that the validation of proposed models mainly focuses on an aggregated level, which makes it ignore performance gap differences between buildings. The models exist the problem of prebound- and rebound effects due to uncertainty factor. Occupant's willingness to retrofit is ignored in all three categories of models, which can lead to the prediction result deviate from the actual situation in a certain extent. This paper promotes the development of models for predicting energy-saving potential for large-scale buildings, and help to formulate appropriate strategies for the retrofit of existing buildings

    Lipid production in batch and fed-batch cultures of <it>Rhodosporidium toruloides</it> from 5 and 6 carbon carbohydrates

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    <p>Abstract</p> <p>Background</p> <p>Microbial lipids are a potential source of bio- or renewable diesel and the red yeast <it>Rhodosporidium toruloides</it> is interesting not only because it can accumulate over 50% of its dry biomass as lipid, but also because it utilises both five and six carbon carbohydrates, which are present in plant biomass hydrolysates.</p> <p>Methods</p> <p><it>R. toruloides</it> was grown in batch and fed-batch cultures in 0.5 L bioreactors at pH 4 in chemically defined, nitrogen restricted (C/N 40 to 100) media containing glucose, xylose, arabinose, or all three carbohydrates as carbon source. Lipid was extracted from the biomass using chloroform-methanol, measured gravimetrically and analysed by GC.</p> <p>Results</p> <p>Lipid production was most efficient with glucose (up to 25 g lipid L<sup>−1</sup>, 48 to 75% lipid in the biomass, at up to 0.21 g lipid L<sup>−1</sup> h<sup>−1</sup>) as the sole carbon source, but high lipid concentrations were also produced from xylose (36 to 45% lipid in biomass). Lipid production was low (15–19% lipid in biomass) with arabinose as sole carbon source and was lower than expected (30% lipid in biomass) when glucose, xylose and arabinose were provided simultaneously. The presence of arabinose and/or xylose in the medium increased the proportion of palmitic and linoleic acid and reduced the proportion of oleic acid in the fatty acids, compared to glucose-grown cells.</p> <p>High cell densities were obtained in both batch (37 g L<sup>−1</sup>, with 49% lipid in the biomass) and fed-batch (35 to 47 g L<sup>−1</sup>, with 50 to 75% lipid in the biomass) cultures. The highest proportion of lipid in the biomass was observed in cultures given nitrogen during the batch phase but none with the feed. However, carbohydrate consumption was incomplete when the feed did not contain nitrogen and the highest total lipid and best substrate consumption were observed in cultures which received a constant low nitrogen supply.</p> <p>Conclusions</p> <p>Lipid production in <it>R. toruloides</it> was lower from arabinose and mixed carbohydrates than from glucose or xylose. Although high biomass and lipid production were achieved in both batch and fed-batch cultures with glucose as carbon source, for lipid production from mixtures of carbohydrates fed-batch cultivation was preferable. Constant feeding was better than intermittent feeding. The feeding strategy did not affect the relative proportion of different fatty acids in the lipid, but the presence of C5 sugars did.</p

    Search for emission of gamma-ray bursts with the ARGO-YBJ detector

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    The ARGO-YBJ experiment has been designed to decrease the energy threshold of tipical Extensive Air Shower arrays by exploiting the high altitude location (Tibet P.R. China, 4300 m a.s..l.) and the full coverage. The lower energy limit of the detector (a few GeV) is reached with the single particle technique, recording the counting rate at fixed time intervals. We present the first results concerning the search for emission from Gamma-Ray Bursts in coincidence with satellite detections

    Measurement of the D--->K^-\pi^+ strong phase difference in \psi(3770)--->D^0\antiD^0

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    Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective

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