65 research outputs found
Numerical Simulation of a Magnetocaloric Heat Pump for Domestic Hot Water Production in Residential Buildings
Numerical Simulation of a Magnetocaloric Heat Pump for Domestic Hot Water Production in Residential Buildings
The BSUIN project
Baltic Sea Underground Innovation Network (BSUIN) is an European Union funded project that
extends capabilities of underground laboratories. The aim of the project is to join efforts in making
the underground laboratories in the Baltic Sea Region’s more accessible for innovation, business
development and science by improving the availability of information about the underground
facilities, service offerings, user experience, safety and marketing.The development of standards
for the characterization of underground laboratories will allow to compared them with each other.
This will help you choose the best places for physical measurements such as neutrino physics or
searching for dark matter. The project concerns laboratories where so far no measurements have
been made, and even undergrounds where there are no organized laboratories yet.The description
of the BSUIN project and the first results of characterization of natural radioactive background in
underground laboratories will be presented Ë™ The BSUIN Project is funded by Interreg Baltic Sea
funding cooperation [2]
Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives
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
Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives
Mini-EUSO experiment to study UV emission of terrestrial and astrophysical origin onboard of the International Space Station
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 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 (), 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 SiPM imaging array
Innate Immune Response to Rift Valley Fever Virus in Goats
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
Fluid Ontologies in the Search for MH370
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
Machine learning for estimation of building energy consumption and performance:a review
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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