Mansfield, L. A. et al. Predicting global patterns of long-term climate change from short-term simulations using machine learning. NPJ Clim. Atmos. Sci. 3, 44 (2020).
Alali, Y., Harrou, F. & Sun, Y. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci. Rep. 12, 2467 (2022).
Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (PublicAffairs, 2019).
Weber, M. The Theory of Social and Economic Organization (Simon & Schuster, 2009).
Salganik, M. J. et al. Measuring the predictability of life outcomes with a scientific mass collaboration. Proc. Natl Acad. Sci. USA 117, 8398–8403 (2020).
Lynge, E., Sandegaard, J. L. & Rebolj, M. The Danish National Patient Register. Scand. J. Public Health 39, 30–33 (2011).
Pedersen, C. B. The Danish
Jordan Eleniak, a Métis university student who grew up in Lac La Biche, won’t remember a summer without a blue-environmentally friendly algae bloom. He designed a bacterial fuel mobile to assist communities forecast them.
Blue-environmentally friendly algae are also regarded as cyanobacteria. Blue-inexperienced algae blooms are a pure phenomenon, but when they reach extreme degrees, they can turn into harmful to the ecosystem and to aquatic animals.
This summer, Eleniak had a probability to use his engineering competencies and style and design a unit that detects and forecasts algae blooms when he interned with the Countrywide Analysis Council.
The system of detection focuses on measuring the microbial action inside a hydrogen gasoline cell, reported Adam Bergren, an performing director of investigation and improvement at the NRC nanotechnology study centre, who supervised and mentored Eleniak for the duration of the summer season.
The mobile normally takes microorganisms and microorganisms purely natural