Friday, December 15, 2017



Smoke from wildfires may be surprisingly deadly, scientists report

The hills above Santa Barbara, Calif., are shrouded in smoke from the Thomas Fire. From WaPo.

“What burns matters,” said Manvendra Dubey, a researcher at Los Alamos National Laboratory, who is also working on the problem of wildfire smoke and its consequences. Dubey underscored the complex chemistry of the smoke that emanates from different types of fires at the annual American Geophysical Union meeting in New Orleans.

So there is much more to learn about the dangers of wildfire smoke — but based on the little we know so far, it sounds like a serious threat, and one that could grow even worse in the future. (Full Story)




Raspberry Pi modules combine to let developers test supercomputer software


Raspberry Pi module.

Software developers who write (or want to write) software for supercomputers just got a huge holiday present: a way to test their software for bugs without having to use a supercomputer. It’s an affordable, scalable device consisting of thousands of inexpensive Raspberry Pi nodes and serves as a powerful high-performance-computing testbed for system-software developers, researchers, and others who lack machine time on the world’s fastest supercomputers.

“It’s not like you can keep a petascale machine around for R&D work in scalable systems software,” says Gary Grider, leader of the High Performance Computing Division at Los Alamos National Laboratory, home of the Trinity supercomputer. (Full Story)

Also from Machine Design this week:

Electrically stimulated quantum dots amplify light


Quantum dot laser, LANL photo.       

Los Alamos scientists have shown they can amplify light using electrically excited films of quantum dots (i.e., chemically synthesized semiconductor nanocrystals). The films are put into devices much like the now-ubiquitous light-emitting diodes (LEDs), but, in this case, they are designed to sustain the high current densities required for achieving the optical-gain regime.

Laser diodes are common and can be found in laser pointers, barcode readers, and the like. A key element of such devices is an optical-gain medium, which amplifies incident light rather than absorbing it. (Full Story)



10 surprising ways machine learning is being used today

Illustration from InformationWeek. 

About 10,000 people die in earthquakes each year, so researchers are always on the hunt for ways to predict earthquakes and their magnitude. A pair of scientists at Los Alamos National Laboratory have taken a crucial first step in that direction.

The researchers created a laboratory earthquake simulation: a model consisting of blocks separated by a chasm, or “fault line.” They then trained a machine learning algorithm to detect acoustic emissions from the model. In other words, by learning what an earthquake “sounds” like just before it happens, the model knew how to “listen” for future earthquakes. (Full Story)



Four scientists win Los Alamos Medal

The Los Alamos Medal was established in 2001, LANL photo.

Los Alamos National Laboratory will award four former researchers with the Los Alamos Medal for their scientific contributions. Scott Cram, Larry Deaven, Robert Moyzis and Howard Menlove will receive the award, the highest honor bestowed by the Laboratory.

The team of Cram, Deaven, and Moyzis are recognized for their work sequencing the human genome and Menlove for his work on methods and instruments used for treaty verification.

The Laboratory will hold an award ceremony in early 2018 to honor the recipients. (Full Story)

Also from the Daily Post

Chamisa students compete in electric car challenge



With support from Los Alamos National Laboratory, two teams of Chamisa Elementary School 6th graders participated in the New Mexico Electric Car Challenge and proudly brought home a 3rd place trophy.

Susan Hettinga, a teacher at Chamisa Elementary School, was the “ringleader” of the eight students who participated - Victoria Raven, Lia Rushton, Maya Carpenter, Rosario Dodd, Seth Javernick, Owen Wylie, Julianna McCabe and Ella Javernick. (Full Story)

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