The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets.

Scientists looking to create a new generation of supercomputers are looking for inspiration from the most complex and energy-efficient computer ever built: the human brain. In some of their initial forays into making brain-inspired computers, researchers are looking at different nonbiological materials whose properties could be tailored to show evidence of learning-like behaviors. These materials could form the basis for hardware that could be paired with new software algorithms to enable more potent, useful and energy-efficient artificial intelligence (AI). In a new study led by scientists from Purdue University, researchers have exposed oxygen deficient nickel oxide to brief electrical pulses and elicited two different electrical responses that are similar to learning. The result is an all-electrically-driven system that shows these learning behaviors, said Rutgers University professor Shriram Ramanathan.

The Research Brief is a short take about interesting academic work. A unique material, nickel oxide demonstrates the ability to learn things about its environment in a way that emulates the most basic learning abilities of animals, as my colleagues and I describe in a new paper. For over half a century, neuroscientists have studied sea slugs to understand basic animal learning. Two fundamental concepts of learning are habituation and sensitization. Habituation occurs when an organism's response to a repeated stimulus continuously decreases.

A new study has found that a material can mimic the sea slug's most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms. The study, publishing this week in the Proceedings of the National Academy of Sciences, was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and Argonne National Laboratory. "Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism's survival," said Shriram Ramanathan, a Purdue professor of materials engineering. "We want to take advantage of that mature intelligence in animals to accelerate the development of AI." Two main signs of intelligence that neuroscientists have learned from sea slugs are habituation and sensitization.

You are free to share this article under the Attribution 4.0 International license. For artificial intelligence to get any smarter, it needs first to be as intelligent as one of the simplest creatures in the animal kingdom: the sea slug. Researchers have found that a material can mimic the sea slug's most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms. "Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism's survival," says Shriram Ramanathan, a professor of materials engineering at Purdue University.

Researchers mimic the animal kingdom's most basic signs of intelligence in quantum material WEST LAFAYETTE, Ind. -- For artificial intelligence to get any smarter, it needs first to be as intelligent as one of the simplest creatures in the animal kingdom: the sea slug. A new study has found that a material can mimic the sea slug's most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms. The study, publishing this week in the Proceedings of the National Academy of Sciences, was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and Argonne National Laboratory. "Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism's survival," said Shriram Ramanathan, a Purdue professor of materials engineering.