This metamaterial-based e-skin integrates multiple sensory inputs, including pre-contact detection and self-powered operation, advancing wearable and robotic technologies.
In the ongoing battle against cancer, a new AI approach is being explored that holds the potential to revolutionize the future of personalized cancer treatments.
The technology, which is an amalgamation of artificial intelligence, molecular dynamics simulations, and network analysis, aims to predict the binding sites on cancer-related proteins. This will pave the way for a faster development of treatments tailored for individual cancer patients.
The study was led by Dr. Rafael Bernardi, an associate professor of biophysics in the Department of Physics at Auburn University. As part of a collaborative effort with the University of Basel and ETH Zurich, the team is breaking barriers on how we understand and fight cancer.
But what if you’re a manufacturer without the budget, bandwidth or time to invest in advanced digital transformation right now? You can still take practical steps to move forward. Start with fundamental data collection and analytic tools to lay the groundwork. Leveraging visibility solutions like barcode scanning, wearables or other basic Internet of Things (IoT) devices can help monitor machines and provide insights and improvements.
Quality is the final piece of the equation. Once you’re further down the path to transformation, implement visibility solutions and augment and upskill workers with technology to optimize quality. To drive quality even further, add advanced automation solutions. You don’t have to boil the ocean on your digital transformation journey—take it one step at a time from wherever you’re starting.
Most manufacturers (87%) in Zebra’s study agree it’s a challenge to pilot new technologies or move beyond the pilot phase, yet they plan to advance digital maturity by 2029. With the right technology tools and solutions in place to advance visibility, augment workers and optimize quality, they will get there.
The University of Texas at Austin this week announced that it has opened its undergraduate robotics program to high school applicants. The honors program is one of the first in the U.S. that allows incoming freshmen to apply for the program as part of their initial admission application. It’s a clear indication that robotics is no longer in the realm of hyper-specialized graduate and doctorate programs.
The minor is tied to a handful of other majors, including aerospace engineering, electrical and computer engineering, computational engineering, computer science and mechanical engineering. Each forms a foundational piece of an eventual robotics major. With the rise of robotics in high school STEAM teaching, the program is designed to maintain incoming freshmen’s interest in the growing space.
A wheeled bot rolls across the floor. A soft-bodied robotic star bends its five legs, moving with an awkward shuffle.
Powered by conventional electricity via plug or battery, these simple robotic creations would be unremarkable, but what sets these two robots apart is that they are controlled by a living entity: a king oyster mushroom.
By growing the mushroom’s mycelium, or rootlike threads, into the robot’s hardware, a team led by Cornell University researchers has engineered two types of robots that sense and respond to the environment by harnessing electrical signals made by the fungus and its sensitivity to light.
White House declares BGP security issues a national priority – BGP handles routing for the entire internet.
A Dangerous Network: The Border Gateway Protocol has been the primary routing technology for the internet for at least three decades. Like other fundamental internet protocols developed in the 1980s, BGP was not originally designed with security in mind – and it shows.
After numerous incidents related to traffic routing among different autonomous systems, the White House has decided to address the security issues of the Border Gateway Protocol. The US administration has tasked the White House Office of the National Cyber Director with developing a roadmap to enhance the security of routing procedures managed through BGP.
The authors present DPAD, a deep learning method, for dynamical neural–behavioral modeling. It dissociates behaviorally relevant neural dynamics, better predicts neural–behavioral data and reveals insight into where their nonlinearities can be isolated.
