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Category: robotics/AI – Page 2,045

One of the most difficult challenges in treating the brain cancer glioblastoma is that few drugs can pass through the blood-brain barrier. Scientists at Cedars-Sinai in Los Angeles have developed a system to circumvent this hurdle—one that combines a powerful immuno-oncology drug with a polymer-based delivery vehicle that can cross the blood-brain barrier.

The researchers showed that this “nano-immunotherapy” treatment crossed the blood-brain barrier in mouse models of glioblastoma, and that it stopped tumor cells from multiplying. They published their findings in the journal Nature Communications.

The Cedars-Sinai team used the polymer scaffold to deliver two types of immune checkpoint inhibitors, blocking either CTLA-4 or PD-1. When injected into the bloodstream of mice, the drugs quickly infiltrated brain tumors, but not healthy brain tissue, the researchers reported.

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Researchers have successfully created a model of the Universe using artificial intelligence, reports a new study.

Researchers seek to understand our Universe by making to match observations. Historically, they have been able to model simple or highly simplified physical systems, jokingly dubbed the “spherical cows,” with pencils and paper. Later, the arrival of computers enabled them to model complex phenomena with . For example, researchers have programmed supercomputers to simulate the motion of billions of particles through billions of years of cosmic time, a procedure known as the N-body simulations, in order to study how the Universe evolved to what we observe today.

“Now with , we have developed the first neural network model of the Universe, and demonstrated there’s a third route to making predictions, one that combines the merits of both analytic calculation and numerical simulation,” said Yin Li, a Postdoctoral Researcher at the Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, and jointly the University of California, Berkeley.

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Researchers from North Carolina State University have developed a technique for measuring speed and distance in indoor environments, which could be used to improve navigation technologies for robots, drones—or pedestrians trying to find their way around an airport. The technique uses a novel combination of Wi-Fi signals and accelerometer technology to track devices in near-real time.

“We call our approach Wi-Fi-assisted Inertial Odometry (WIO),” says Raghav Venkatnarayan, co-corresponding author of a paper on the work and a Ph.D. student at NC State. “WIO uses Wi-Fi as a velocity sensor to accurately track how far something has moved. Think of it as sonar, but using radio waves, rather than sound waves.”

Many devices, such as smartphones, incorporate technology called inertial measurement units (IMUs) to calculate how far a has moved. However, IMUs suffer from large drift errors, meaning that even minor inaccuracies can quickly become exaggerated.

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A team of Australian researchers has designed a reliable strategy for testing physical abilities of humanoid robots—robots that resemble the human body shape in their build and design. Using a blend of machine learning methods and algorithms, the research team succeeded in enabling test robots to effectively react to unknown changes in the simulated environment, improving their odds of functioning in the real world.

The findings, which were published in a joint publication of the IEEE and the Chinese Association of Automation Journal of Automatica Sinica in July, have promising implications in the broad use of in fields such as healthcare, education, disaster response and entertainment.

“Humanoid robots have the ability to move around in many ways and thereby imitate human motions to complete complex tasks. In order to be able to do that, their stability is essential, especially under dynamic and unpredictable conditions,” said corresponding author Dacheng Tao, Professor and ARC Laureate Fellow in the School of Computer Science and the Faculty of Engineering at the University of Sydney.

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A new generation of swarming robots which can independently learn and evolve new behaviors in the wild is one step closer, thanks to research from the University of Bristol and the University of the West of England (UWE).

The team used artificial evolution to enable the robots to automatically learn swarm behaviors which are understandable to humans. This new advance published today in Advanced Intelligent Systems, could create new robotic possibilities for environmental monitoring, disaster recovery, infrastructure maintenance, logistics and agriculture.

Until now, artificial evolution has typically been run on a computer which is external to the swarm, with the best strategy then copied to the robots. However, this approach is limiting as it requires external infrastructure and a laboratory setting.

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Wearing a flower brooch that blooms before your eyes sounds like magic. KAIST researchers have made it real with robotic muscles.

Researchers have developed an ultrathin, for soft robotics. The advancement, recently reported in the journal Science Robotics, was demonstrated with a robotic blooming flower brooch, dancing robotic butterflies and fluttering tree leaves on a kinetic art piece.

The robotic equivalent of a that can move is called an . The actuator expands, contracts or rotates like using a stimulus such as electricity. Engineers around the world are striving to develop more dynamic actuators that respond quickly, can bend without breaking, and are very durable. Soft, robotic muscles could have a wide variety of applications, from wearable electronics to advanced prosthetics.

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