Exposure data available to developers of earthquake loss models are often very crudely aggregated spatially, and in such cases very considerable effort can be required to refine the geographical resolution of the building stock inventory. The influence of the geographical resolution of the exposure data for the Sea of Marmara region in Turkey is explored using several different levels of spatial aggregation to estimate the losses due to a single earthquake scenario. The results show that the total damage over an urban area, expressed as a mean damage ratio (MDR), is rather insensitive to the spatial resolution of the exposure data if a sufficiently large number of ground-motion simulations are used. However, the variability of the MDR estimates does reduce as the spatial resolution becomes higher, reducing the number of simulations required, although there appears to be a law of diminishing returns in going to very high exposure data resolution. This is largely due to the inherent and irreducible spatial variability of ground motion, which suggests that if only mean MDR estimates are needed, the effort required to refine the spatial definition of exposure data is not justified.
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This white paper explores the challenges to integrating Artificial Intelligence (AI) solutions into clinical practice, addressing perspectives on a) human factors, b) AI technologies, and c) integration in clinical workflow. Through creating insights into these perspectives, we aim to contribute to the development and implementation of innovative, human-centred AI solutions that provide tangible benefits to patients, healthcare providers and society as a whole.
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Het lectorenplatform Inzet van Technologie voor Gezondheid en Zorg (PIT) richt zich op de implementatie van technologische innovaties in de dagelijkse praktijk met zorg- en welzijnsprofessionals en burgers. In dit projectplan voor 2023-2026 staat beschreven wat de motivatie van PIT is, welke doelstellingen het nastreeft, hoe PIT georganiseerd is en wat de strategische agenda is.
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Instead of using a passive AC power grid for low power applications, this paper describes a smart plug for DC networks that is capable of providing the correct power to a device (up to 100W) and that allows for communication between different plugs and monitoring of energy consumption across the DC network using the Ethernet protocol in conjunction with a signal modulator to adapt the signals to the DC network. The ability to monitor consumption on a device-per-device basis allows for closer monitoring of in-house energy use and provides an easily scalable platform to monitor consumption at a macro level. In order to make this paper attractive for the consumer market and easily integrable with existing consumer devices, a generally compatible solution is needed. To meet these demands and to take advantage of the trend of charging consumer devices through USB, we opted for the recently adapted USB Power Delivery standard. This standard allows devices to communicate with the plug and demand a specific voltage and current needed for the device to operate. The purpose of this paper is to give the reader insight in the development of a proof of concept of the smart DC/DC power plug. 10.1109/DUE.2014.6827761
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