Dr. Hun-Gi Jung and his research team at the Center for Energy Storage Research of the Korea Institute of Science and Technology (KIST, President Lee Byung Gwon) have announced the development of silicon anode materials that can increase battery capacity four-fold in comparison to graphite anode materials and enable rapid charging to more than 80% capacity in only five minutes. When applied to batteries for electric vehicles, the new materials are expected to more than double their driving range.

 

The batteries currently installed in mass-produced electric vehicles use graphite anode materials, but their low capacity contributes to electric vehicles’ having a shorter driving range than vehicles with internal combustion engines. Consequently, silicon, with an energy storage capacity 10-times greater than graphite, has drawn attention as a next-generation anode material for the development of long-range electric vehicles. However, silicon materials have not yet been commercialized because their volume expands rapidly and storage capacity decreases significantly during charge and discharge cycles, which limits commercialization. A number of methods have been suggested for enhancing the stability of silicon as an anode material, but the cost and complexity of these methods have prevented silicon from replacing graphite.

 

 

Sourced through Scoop.it from: eurekalert.org

The FT has enlisted the help of readers, researchers and entrepreneurs to find 50 new ideas that will shape the world in the future.

 

 

Read all 50 ideas

Sourced through Scoop.it from: www.ft.com

ESA’s Solar Orbiter is now on its way to the sun, beginning a nearly two-year journey.

 

A new sungazing spacecraft has launched on a mission to chart the sun’s unexplored polar regions and to understand how our star creates and controls the vast bubble of plasma that envelops the solar system.

 

At 11:03 pm ET on February 9, 2020, the European Space Agency’s Solar Orbiter rocketed away from Cape Canaveral, Fla. The spacecraft now begins a nearly two-year convoluted journey — getting two gravity assists from Venus and one from Earth — to an orbit that will repeatedly take it a bit closer to the sun than Mercury gets.

 

 

Sourced through Scoop.it from: www.sciencenews.org

Jigsaw, a technology incubator at Google, has released an experimental platform called Assembler to help journalists and front-line fact-checkers quickly verify images.How it works: Assembler combines several existing techniques in academia for detecting common manipulation techniques, including changing image brightness and pasting copied pixels elsewhere to cover up something while retaining the same visual texture.

Sourced through Scoop.it from: www.technologyreview.com

Original announcement

 

In a population of animals or plants, genetic diversity can decline much more quickly than species diversity in response to various stress factors: disease, changes to habitat or climate, and so on. Yet not much is known about fish genetic diversity around the world.

 

Help on that front is now on the way from an international team of scientists from French universities and ETH Zurich. They have produced the first global distribution map for genetic diversity among freshwater and marine fish. Furthermore, they identified the environmental factors that are instrumental in determining the distribution of genetic diversity. Their study was recently published in the journal Nature Communications.

 

 

Sourced through Scoop.it from: www.eurekalert.org

A drug designed entirely by artificial intelligence is about to enter clinical human trials for the first time. The drug, which is intended to treat obsessive-compulsive disorder (OCD), was discovered using AI systems from Oxford-based biotech company Exscientia. While it would usually take around four and a half years to get a drug to this stage of development, Exscientia says that by using the AI tools it’s taken less than 12 months.

 

The drug, known as DSP-1181, was created by using algorithms to sift through potential compounds, checking them against a huge database of parameters, including a patient’s genetic factors. Speaking to the BBC, Exscientia chief executive Professor Andrew Hopkins described the trials as a "key milestone in drug discovery" and noted that there are "billions" of decisions needed to find the right molecules for a drug, making their eventual creation a "huge decision." With AI, however, "the beauty of the algorithm is that they are agnostic, so can be applied to any disease."

 

Sourced through Scoop.it from: www.engadget.com

Researchers at Massachusetts Institute of Technology (MIT) and Qatar Computing Research Institute (QCRI) have developed a model powered by AI. The AI model is designed to tag road features in digital maps using satellite imagery. This AI-driven RoadTagger model combines a convolutional neural network (CNN) and a graph neural network (GNN) to automatically envisage the number of lanes and road types concealed by obstructions, improving GPS navigation, especially in countries with limited map data.

 

The model helps drivers in incorporating information about parking spots, while mapping bicycle lanes that can assist cyclists to negotiate busy city streets. Providing updated information on road conditions, the RoadTagger model can also improve planning for disaster relief.

 

Unlike other GPS navigation systems, RoadTagger makes use of an amalgamation of neural network architectures to automatically predict the number of lanes and road types, including residential or highway, even when roads can be blocked by trees or buildings.

Sam Madden, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in the Computer Science and AI Laboratory (CSAIL) says, “Most updated digital maps are from places that big companies care the most about. If you’re in places they don’t care about much, you’re at a disadvantage with respect to the quality of map. Our goal is to automate the process of generating high-quality digital maps, so they can be available in any country.”

 

When testing RoadTagger on occluded roads from digital maps of 20 US cities, the model reckoned lane numbers with 77 percent accuracy and inferred road types with 93 percent accuracy. Also, the researchers are planning to enable the model to foresee other features, such as parking spots and bike lanes.

 

The model relies on CNN and GNN, where GNNs form relationships between connected nodes in a graph, CNNs take as input raw satellite images of target roads. RoadTagger is based on an end-to-end model, meaning it is fed only raw data and automatically generates output, without human intervention. This combined architecture of CNN and GNN signifies a more human-like intuition, researchers noted.

Sourced through Scoop.it from: www.analyticsinsight.net