Nettsider med emneord «Statistical methods»

Economic models used for forecasting and to aid policy decisions have been estimated by the use of data from before the Covid-19 pandemic and the ensuing lockdowns, drop in economic activity and surge in unemployment. An important question for model developers and users is therefore how the empirical relationships that represented normal behavior of firms and households before Covid-19 have been affected by the pandemic and by the policy responses.

A six-year project with the goal to develop and use machine learning to improve the way social scientists can answer classic as well as emerging questions in economics that require the use of large datasets.

The spatial configuration of continents and its temporal evolution exert a fundamental control on Earth’s evolution. Before 130 Ma, plate motions can only be quantified through the study of paleomagnetism, however, individual paleomagnetic data cannot constrain longitude.

Accurate mapping of surface greenhouse gas fluxes is necessary for the validation and calibration of climate models. In this project, we develop a novel framework using drone observations and machine learning to estimate greenhouse gas fluxes at a regional scale.

LATICE aims to advance the knowledge base concerning land atmosphere interactions through improved model representation of snow, permafrost, hydrology and large-scale vegetation processes representative of high latitudes, including; 1) New ground observations (gap filling using ML); 2) Land surface model parametrization using data science methods; 3) Seasonal snow cover dynamics using data assimilation and Earth observations (e.g. satellite data, drones).

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers. The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming.

Exploring the fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data we need to perform very computationally expensive calculations in quantum field theory (QFT).

The aim of this project is to determine the effectiveness of antidepressant treatment in pregnant and postpartum women, as well as the longer-term metabolic safety of these drugs in pregnancy on the offspring.

The COVID-19 pandemic resulted in an unprecedented need for pharmacoepidemiological studies related to infectious disease, mental health, medication use, and vaccination.

G-protein coupled receptor (GPCRs) form the largest superfamily of membrane proteins in human. 34% of the marketed small molecule drugs bind to GPCRs. Tens of millions of compounds are commercially available for screening against GPCRs in experimental setting, which is impractical for academia and industry.

For maritime safety surveillance we develop new approaches
based on the availability of large arrays of sensors, which
monitor condition and performance of vessels, machinery, or
power systems.

AI, statistical models and machine learning methods can often be seen as black boxes to those who construct the model and/or to those who use or are exposed to the methods.

In a wide range of applications, monitoring data streams for faults or changes in behavior (called anomalies) is of great importance.

In HIDDEN we employ atomistic simulations (i) to establish the presence or not of a hidden geochemical reservoir in the deep mantle that can store noble gases, (ii) to calculate the permeability of the core-mantle boundary throughout geological time with respect to noble gases, (iii) to determine the exchanges of noble gases between the mantle and the core during the core formation, and (iv) to give estimates of fluxes of noble gases through the Earth’s mantle throughout the geological time.

The main objective of this project is to provide a geoscientific solution for increasing the renewable (geothermal) energy production in northwest Romania. This will lead to a decrease in actual CO2 emissions generated by electrical and thermal energy production using fossil fuel.

Heterogeneous catalysis is a key enabling technology for the green transition. Industrial catalysts are always shaped into millimeter-sized catalyst objects.

EBP-Nor will sequence and catalogue all eukaryotic species occurring in Norway, an estimated 45,000 species, to contribute to the global effort. Graph-based algorithms in assemblers and scaffolders are essential for this work.