99 research outputs found

    North European Understanding of Zero Energy/Emission Buildings

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    Susceptibility of Pigs and Chickens to SARS Coronavirus

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    An outbreak of severe acute respiratory syndrome (SARS) in humans, associated with a new coronavirus, was reported in Southeast Asia, Europe, and North America in early 2003. To address speculations that the virus originated in domesticated animals, or that domestic species were susceptible to the virus, we inoculated 6-week-old pigs and chickens intravenously, intranasally, ocularly, and orally with 106 PFU of SARS-associated coronavirus (SARS-CoV). Clinical signs did not develop in any animal, nor were gross pathologic changes evident on postmortem examinations. Attempts at virus isolation were unsuccessful; however, viral RNA was detected by reverse transcriptase-polymerase chain reaction in blood of both species during the first week after inoculation, and in chicken organs at 2 weeks after inoculation. Virus-neutralizing antibodies developed in the pigs. Our results indicate that these animals do not play a role as amplifying hosts for SARS-CoV

    Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives

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    Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments.Comment: 30 pages, 14 figures, 4 table

    Mini-EUSO experiment to study UV emission of terrestrial and astrophysical origin onboard of the International Space Station

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    International audienceMini-EUSO will observe the Earth in the UV range (300 - 400 nm) offering the opportunity to study a variety of atmospheric events such as Transient LuminousEvents (TLEs), meteors and marine bioluminescence. Furthermore it aims to search for Ultra High Energy Cosmic Rays (UHECR) above 102110^{21} eV and Strange Quark Matter (SQM).The detector is expected to be launched to the International Space Station in August 2019 and look at the Earth in nadir mode from the UV-transparent window of the Zvezda module of the International Space Station. The instrument comprises a compact telescope with a large field of view (44∘44^{\circ}), based on an optical system employing two Fresnel lenses for lightcollection. The light is focused onto an array of 36 multi-anode photomultiplier tubes (MAPMT), for a total of 2304 pixels and the resulting signal is converted into digital, processed and stored viathe electronics subsystems on-board. In addition to the main detector, Mini-EUSO contains two ancillary cameras for complementary measurements in the near infrared (1500 - 1600 nm) and visible (400 - 780 nm) range and also a 8×88 \times 8 SiPM imaging array

    Fluid Ontologies in the Search for MH370

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    This paper gives an account of the disappearance of Malaysian Airways Flight MH370 into the southern Indian Ocean in March 2014 and analyses the rare glimpses into remote ocean space this incident opened up. It follows the tenuous clues as to where the aeroplane might have come to rest after it disappeared from radar screens – seven satellite pings, hundreds of pieces of floating debris and six underwater sonic recordings – as ways of entering into and thinking about ocean space. The paper pays attention to and analyses this space on three registers – first, as a fluid, more-than-human materiality with particular properties and agencies; second, as a synthetic situation, a composite of informational bits and pieces scopically articulated and augmented; and third, as geopolitics, delineated by the protocols of international search and rescue. On all three registers – as matter, as data and as law – the ocean is shown to be ontologically fluid, a world defined by movement, flow and flux, posing intractable difficulties for human interactions with it

    Innate Immune Response to Rift Valley Fever Virus in Goats

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    Rift Valley fever (RVF), a re-emerging mosquito-borne disease of ruminants and man, was endemic in Africa but spread to Saudi Arabia and Yemen, meaning it could spread even further. Little is known about innate and cell-mediated immunity to RVF virus (RVFV) in ruminants, which is knowledge required for adequate vaccine trials. We therefore studied these aspects in experimentally infected goats. We also compared RVFV grown in an insect cell-line and that grown in a mammalian cell-line for differences in the course of infection. Goats developed viremia one day post infection (DPI), which lasted three to four days and some goats had transient fever coinciding with peak viremia. Up to 4% of peripheral blood mononuclear cells (PBMCs) were positive for RVFV. Monocytes and dendritic cells in PBMCs declined possibly from being directly infected with virus as suggested by in vitro exposure. Infected goats produced serum IFN-γ, IL-12 and other proinflammatory cytokines but not IFN-α. Despite the lack of IFN-α, innate immunity via the IL-12 to IFN-γ circuit possibly contributed to early protection against RVFV since neutralising antibodies were detected after viremia had cleared. The course of infection with insect cell-derived RVFV (IN-RVFV) appeared to be different from mammalian cell-derived RVFV (MAM-RVFV), with the former attaining peak viremia faster, inducing fever and profoundly affecting specific immune cell subpopulations. This indicated possible differences in infections of ruminants acquired from mosquito bites relative to those due to contact with infectious material from other animals. These differences need to be considered when testing RVF vaccines in laboratory settings

    Machine learning for estimation of building energy consumption and performance:a review

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    Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance
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