News by Subject Mathematics

A just-published study coins a new metric: the "mortality cost of carbon." That is, how many future lives will be lost--or saved--depending on whether we increase or decrease our current carbon emissions. If the numbers hold up, they are quite high.

University of Washington scientists have developed a statistical framework that incorporates key COVID-19 data -- such as case counts and deaths due to COVID-19 -- to model the true prevalence of this disease in the United States and individual states. Their approach projects that in the U.S. as many as 60% of COVID-19 cases went undetected as of March 7, 2021, the last date for which the dataset they employed is available.

Advanced technologies have been used to solve a long-standing mystery about why some people develop serious illness when they are infected with the malaria parasite, while others carry the infection asymptomatically.

Researchers created a simulation of a deep-sea sponge and how it responds to and influences the flow of water. The work revealed a profound connection between the sponge's structure and function, shedding light on both the basket sponge's ability to withstand the dynamic forces of the surrounding ocean and its ability to create a vortex within the body cavity "basket." These properties may help for the design of ships, planes and skyscrapers of the future.

Researchers are the first to model COVID-19 completion versus cessation in clinical trials using machine learning algorithms and ensemble learning. They collected 4,441 COVID-19 trials from ClinicalTrials.gov to build a testbed with 693 dimensional features created to represent each clinical trial. These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended. Stakeholders can leverage the predictions to plan resources, reduce costs, and minimize the time of the clinical study.

For the first time an autonomously flying quadrotor has outperformed two human pilots in a drone race. The success is based on a novel algorithm that was developed by researchers of the University of Zurich. It calculates time-optimal trajectories that fully consider the drones' limitations.

Although photovoltaic systems constitute a promising way of harnessing solar energy, power grid managers need to accurately predict their power output to schedule generation and maintenance operations efficiently. Scientists from Incheon National University, Korea, have developed a machine learning-based approach that can more accurately estimate the output of photovoltaic systems than similar algorithms, paving the way to a more sustainable society.