A machine learning approach for mapping the very shallow theoretical geothermal potential
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Based on the audits realised on buildings by the UNIGE-CUEPE in 2007 [2], [3], the present work studies the conditions of the integration of the energy conversion system and of the building refurbishment options to fit with the GLN network and to prepare t ...
The X-ray polarization anisotropy of anomalous scattering in crystals of brominated nucleic acids and selenated proteins is shown to have significant effects on the diffraction data collected at an absorption edge. For conventionally collected single- or mu ...
Producing realistic surface water flow patterns can be difficult for hydrologic models when there is insufficient grid resolution as a result of computational constraints or when available digital elevation model (DEM) data are relatively coarse. This tech ...
Due to daylight variability, a design cannot be thoroughly assessed using single-moment simulations, which is why we need dynamic performance metrics like Daylight Autonomy and Useful Daylight Illuminance. Going one step further, the annual variation in pe ...
This paper presents an empirical study that quantifies the effects of an ecological fiscal reform as recently rejected by the Swiss population. The measure aims to encourage employment and, at the same time, to dissuade from an excessive energy use and the ...
In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...
Evaporation from small reservoirs, wetlands, and lakes continues to be a theoretical and practical problem in surface hydrology and micrometeorology because atmospheric flows above such systems can rarely be approximated as stationary and planar-homogeneous ...
Traditionally, the bursty nature of data sources is not taken in consideration by information theory. Random arrival times typically are assumed to be smoothed out by appropriate source coding, rendering any meaningful analysis of the end-to-end delay impo ...
Geographic Information Science methods and tools are likely to help to extract useful and so far unknown information from large spatially explicit genetic datasets to understand the distribution of diversity among and within sheep and goat breeds. Consider ...
For classification problems, it is important that the classifier is trained with data which is likely to appear in the future. Discriminative models, because of their nature to focus on the boundary between classes rather than data itself, usually do not h ...