Study: Flying keeps getting safer
Reflecting a “Moore’s Law of aviation,” commercial flight has become roughly twice as safe each decade since the 1960s; Covid-19 added a wrinkle, however.
Reflecting a “Moore’s Law of aviation,” commercial flight has become roughly twice as safe each decade since the 1960s; Covid-19 added a wrinkle, however.
Electronic waste is a rapidly growing problem, but this degradable material could allow the recycling of parts from many single-use and wearable devices.
New center taps Institute-wide expertise to improve understanding of, and responses to, sustainability challenges.
The barely-there lunar atmosphere is likely the product of meteorite impacts over billions of years, a new study finds.
Knowing where to look for this signal will help researchers identify specific sources of the potent greenhouse gas.
The work on excitons, originating from ultrathin materials, could impact future electronics and establishes a new way to study these particles through a powerful instrument at the Brookhaven National Laboratory.
The nodes are intended to become part of a widespread sea-ice monitoring network.
More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.
A mathematical method, validated with experimental data, provides a fast, reliable, and minimally invasive way of determining how to treat critical blood pressure changes during surgery or intensive care.
Ultrathin material whose properties “already meet or exceed industry standards” enables superfast switching, extreme durability.
MIT engineers have developed a fast and sustainable method for producing hydrogen fuel using aluminum, saltwater, and coffee grounds.
Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition.
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.