ScienceDaily: Computers & Math News

Researchers have developed a new method for designing large-scale metasurfaces that uses techniques of machine intelligence to generate designs automatically. The method will enable new metasurface designs that can make an impact on virtual or augmented reality, self-driving cars, and machine vision for embarked systems and satellites.

An algorithm can accurately predict which hospitalized patients are at the highest risk of death and require critical care, regardless of immune protection status and virus variant.

A research team has demonstrated an ultrathin silicon nanowire that conducts heat 150% more efficiently than conventional materials used in advanced chip technologies. The device could enable smaller, faster, energy-efficient microelectronics.

Australia has suffered a significant drop in teenage maths proficiency in the past 20 years -- sliding from 11th in the OECD rankings to 29th place out of 38 countries, prompting widespread debate over potential curriculum changes. One researcher says hand gestures could stop the slide.

Engineers are using a cell's surroundings to help biologists make more sense of gene expression information. Their new system could open the door to identifying rare cell types and choosing cancer treatment options with new precision.

Researchers have identified characteristics of people with long COVID and those likely to have it. Scientists used machine learning techniques to analyze an unprecedented collection of electronic health records (EHRs) available for COVID-19 research to better identify who has long COVID.

Harnessing the power of 'phase-change' materials, researchers have demonstrated how reconfigurable metasurfaces -- artificial materials with extraordinary optical properties -- are crucial to the future of nanotechnology.

Scientists identified the most relevant predictive variables for anticipating hospital demand and proposed using an ensemble model based on the average of the predictions of several individual models.

Researchers have developed a new tool to help researchers interpret the clinical significance of somatic mutations in cancer. The tool, known as CancerVar, incorporates machine learning frameworks to go beyond merely identifying somatic cancer mutations and interpret the potential significance of those mutations in terms of cancer diagnosis, prognosis, and targetability.