Ozone levels in the earth's troposphere (the lowest level of our atmosphere) can now be forecasted with accuracy up to two weeks in advance, a remarkable improvement over current systems that can accurately predict ozone levels only three days ahead. The new artificial intelligence system developed in the University of Houston's Air Quality Forecasting and Modeling Lab could lead to improved ways to control high ozone problems and even contribute to solutions for climate change issues.
Russia is the world's largest forest country. Being home to more than a fifth of forests globally, the country's forests and forestry have enormous potential to contribute to making a global impact in terms of climate mitigation. A new study by IIASA researchers, Russian experts, and other international colleagues have produced new estimates of biomass contained in Russian forests, confirming a substantial increase over the last few decades.
Scientists using computer modelling to study SARS-CoV-2 have discovered the virus is most ideally adapted to infect human cells -- rather than bat or pangolin cells -- again raising questions of its origin. In a paper published Scientific Reports, Australian scientists describe how they used high-performance computer modelling of the form of the SARS-CoV-2 virus at the beginning of the pandemic to predict its ability to infect humans and a range of 12 domestic and exotic animals.
Using methods from conservation science, a new analysis suggests that the first case of COVID-19 arose between early October and mid-November, 2019 in China, with the most likely date of origin being November 17. David Roberts of the University of Kent, U.K., and colleagues present these findings in the open-access journal PLOS Pathogens.
The central idea of the study was an analogy between concepts in magnetism and epidemiology in which electron interaction is compared with interaction among people.
Soil liquefaction was a major feature of the 2011 Christchurch, New Zealand earthquake that killed 185 people. Researchers developed a machine learning model to predict the amount of lateral movement that can be expected from liquefaction during a natural hazard event. Their model, trained on Christchurch data, was 70% accurate at determining the amount of displacement that occurred. The researchers used the Frontera supercomputer, one of the world's fastest, to train and test the model.
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.
Supercomputers may no longer be needed to screen candidate materials and perform simulations for a wide variety of theoretical and commercial applications thanks to an easily accessible and computationally inexpensive new machine learning model, which has been initially trained to predict the band gap of solar energy materials as a proof of concept.
Using genome-wide association studies (GWAS) methodology to analyze whole-genome sequencing data of SARS-CoV-2 mutations and COVID-19 mortality data can identify highly pathogenic variants of the virus that should be flagged for containment, according to Harvard T.H. Chan School of Public Health and MIT researchers.
The magnetic sense of migratory birds such as European robins is thought to be based on a specific light-sensitive protein in the eye. In the current edition of the journal Nature, a team centred at the Universities of Oldenburg (Germany) and Oxford (UK) demonstrate that the protein cryptochrome 4, found in birds' retinas, is sensitive to magnetic fields and could well be the long-sought magnetic sensor.