Lifeboat Foundation

Taiga Motors is one of the rare companies working to bring to market an all-electric snowmobile, which is actually a segment of transportation that desperately needs cleaner solutions.

The startup is unveiling today its new lineup of electric snowmobiles with some impressive specs.

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Dallas, Los Angeles, and now an Australian city.

Lyft and Aptiv completed 55,000 self-driving vehicle rides in their first year.

Atomtronics manipulates atoms much in the way that electronics manipulates electrons. It carries the promise of highly compact quantum devices which can measure incredibly small forces or tiny rotations. Such devices might one day be used to monitor Earth’s status by sensing water levels in the desert or in the search for minerals and oil. They will also be used in navigation, when GPS fails on planes or ships due to malicious attacks or simply because it is not available, e.g. in the deep seas. They might also one day act as portable quantum simulators solving complex computational tasks.

Coherent atomtronics manipulates atoms in the form of matterwaves originating from Bose-Einstein condensates (a state of matter in which all the atoms lose their individual identity and become one single quantum state with all the atoms being everywhere in the condensate at the same time). The atoms in these matterwaves behave much more like waves rather than individual particles. These matterwaves can be brought to interfere and thus made to respond to the tiniest changes in their environment such as the difference in gravitational pull between light organic material and heavy iron ore. When compared to light, atoms can be 10 billion times more sensitive, e.g. to rotation or acceleration, when compared to the photons that make up light. This sensitivity depends on the measurement time and—just like Newton’s apple—atoms fall due to Earth’s gravity. This forces the most sensitive interferometers to be very tall, reaching 10 meters and in some cases even 100 meters.

Volkswagen built its ID.R with the intention of showing what the heads in its electric drive division are capable of, and it only took a few months after its unveiling for the zero-emission race car to claim a record at the iconic Pikes Peak. The twin-motor electric racer has now built on this with yet another momentous showing, this time at Nürburgring-Nordschleife, where it has broken the lap record for electric vehicles by a whopping 40.564 seconds.

The new breed of electric hot rodders.

As anyone who has purchased jewelry can attest, platinum is expensive. That’s tough for consumers but also a serious hurdle for a promising source of electricity for vehicles: the hydrogen fuel cell, which relies on platinum.

Now a research team led by Bruce E. Koel, a professor of biological and chemical engineering at Princeton University, has opened a door to finding far cheaper alternatives. In a paper published April 4 in the journal Nature Communications, the researchers reported that a chemical compound based on hafnium worked about 60 percent as effectively as platinum-related materials but at about one-fifth the cost.

“We hope to find something that is more abundant and cheaper to catalyze reactions,” said Xiaofang Yang, principal scientist at HiT Nano Inc. and visiting collaborator at Princeton who is working with Koel on the project.

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Researchers at Oregon State University have found that a chemical mechanism first described more than two centuries ago holds the potential to revolutionize energy storage for high-power applications like vehicles or electrical grids.

The research team led by Xiulei (David) Ji of OSU’s College of Science, along with collaborators at the Argonne National Laboratory, the University of California Riverside, and the Oak Ridge National Laboratory, are the first to demonstrate that diffusion may not be necessary to transport ionic charges inside a hydrated solid-state structure of a battery electrode.

“This discovery potentially will shift the whole paradigm of high-power electrochemical energy storage with new design principles for electrodes,” said Xianyong Wu, a postdoctoral scholar at OSU and the first author of the article.

A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work.

The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn’t. By doing so, Whetstone converts an artificial neural network into a spiking neural network. The tool does this by using an incremental “sharpening process” (hence Whetstone) through each network layer until the activation becomes discrete.

According to Whetstone researcher Brad Aimone, this discrete activation greatly minimizes communication costs between the layers, and thus energy consumption, but with only minimal loss of accuracy. “We continue to be impressed that without dramatically changing what the networks look like, we can get very close to a standard neural net [in accuracy],” he says. “We’re usually within a percent or so on performance.”

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