Lifeboat Foundation

PASADENA, Calif. — With congressional funding and industry support, nuclear thermal propulsion technology is making progress for potential use on future NASA deep space missions, although how it fits into the agency’s exploration architectures remains uncertain.

The House Appropriations Committee approved May 22 a commerce, justice and science (CJS) appropriations bill that offers $22.3 billion for NASA. That funding includes $125 million for nuclear thermal propulsion development within the agency’s space technology program, compared to an administration request for no funding.

“The bill’s investment in nuclear thermal propulsion is critical as NASA works towards the design of a flight demonstration by 2024,” said Rep. Robert Aderholt (R-Ala.), ranking member of the CJS appropriations subcommittee, during that subcommittee’s markup of the bill May 17. He offered similar comments in support of that project at the full committee markup.

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In my view, most of the research so far has placed too much emphasis on cataloguing species names. We’ve been characterizing the human microbiome as if it were a relatively fixed property to be mapped and manipulated — one that is separate from the rest of the body. In fact, I think that interventions that could help to treat conditions such as diabetes, cancer and autoimmune diseases will be discovered only if we move beyond species catalogues and begin to understand the complex and mutable ecological and evolutionary relationships that microbes have with each other and with their hosts.

The dream of microbiome-based medicine requires a fresh approach — an ecological and evolutionary understanding of host-microbe interactions — argues Lita Proctor.

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How you can use Bitcoin without relying on centralized Internet Service Providers.

5G means faster video streaming… and stronger surveillance.

A growing number of devices are now connected to the internet and are capable of collecting, sending and receiving data. This interconnection between devices, referred to as the Internet of Things (IoT), poses serious security threats, as cyberattackers can now target computers and smartphones, but also a vast array of other devices, such as tablets, smart watches, smart home systems, transportation systems and so on.

For the time being, examples of large-scale IoT implementations (e.g. connected infrastructure, cities, etc.) are somewhat limited, yet they could soon become widespread, posing significant risks for businesses and public services that heavily rely on the internet in their daily operations. To mitigate these risks, researchers have been trying to develop security measures to protect devices connected to the internet from wireless network attacks.

To this end, two researchers at Baoji University of Arts and Sciences, in China, have recently developed a new method to defend devices in an IOT environment from wireless network attacks. Their approach, presented in a paper published in Springer’s International Journal of Wireless Information Networks, combines a deep neural network with a model based on game theory, a branch of mathematics that proposes strategies for dealing with situations that entail competition between different parties.

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Wearing a sensor-packed glove while handling a variety of objects, MIT researchers have compiled a massive dataset that enables an AI system to recognize objects through touch alone. The information could be leveraged to help robots identify and manipulate objects, and may aid in prosthetics design.

The researchers developed a low-cost knitted glove, called “scalable tactile glove” (STAG), equipped with about 550 tiny sensors across nearly the entire hand. Each sensor captures pressure signals as humans interact with objects in various ways. A neural network processes the signals to “learn” a dataset of pressure-signal patterns related to specific objects. Then, the system uses that dataset to classify the objects and predict their weights by feel alone, with no visual input needed.

In a paper published in Nature, the researchers describe a dataset they compiled using STAG for 26 common objects—including a soda can, scissors, tennis ball, spoon, pen, and mug. Using the dataset, the system predicted the objects’ identities with up to 76 percent accuracy. The system can also predict the correct weights of most objects within about 60 grams.

Continue reading “Sensor-packed glove learns signatures of the human grasp” »

A beautiful snake of which we know very little.