Engineers develop a vibrating, ingestible capsule that might help treat obesity
Swallowing the device before a meal could create a sense of fullness, tricking the brain into thinking it’s time to stop eating.
Swallowing the device before a meal could create a sense of fullness, tricking the brain into thinking it’s time to stop eating.
This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.
Using generative AI, MIT chemical engineers and chemists created a model that can predict the structures formed when a chemical reaction reaches its point of no return.
The technique could enable restoration efforts and doesn’t require labor-intensive onsite sampling.
In a study that could help fill some holes in quantum theory, the team recreated a “quantum bomb tester” in a classical droplet test.
The realistic model could aid the development of better heart implants and shed light on understudied heart disorders.
The advance opens a path to next-generation devices with unique optical and electronic properties.
Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.
The molecules, known as acenes, could be useful as organic light-emitting diodes or solar cells, among other possible applications.
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
An accordion-textured clay called smectite efficiently traps organic carbon and could help buffer global warming over millions of years.
MIT CSAIL researchers established new connections between combinatorial and continuous optimization, which can find global solutions for complex motion-planning puzzles.
MIT students traveled to Washington to speak to representatives from several federal executive agencies.
The wearable device, designed to monitor bladder and kidney health, could be adapted for earlier diagnosis of cancers deep within the body.
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.