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The device could diagnose disease in the developing world and enable rapid drug screening.

"In 2009, the scientific literature caught up with what top climate scientists have been saying privately for a few years now: * Many of the predicted impacts of human-caused climate change are occurring much faster than anybody expected - particularly ice melt, everywhere you look on the planet. * If we stay anywhere near our current emissions path, we are facing incalculable catastrophes by century's end, including rapid sea level rise, massive wildfires, widespread Dust-Bowlification, large oceanic dead zones, and 9°F warming - much of which could be all but irreversible for centuries. And that's not the worst-case scenario! * The consequences for human health and well being would be extreme. That's no surprise to anybody who has talked to leading climate scientists in recent years, read my book Hell and High Water (or a number of other books), or followed this blog. Still, it is a scientific reality that I don't think more than 2 people in 100 fully grasp, so I'm going to review here the past year in climate science. I'll focus primarily on the peer-reviewed literature, but also look at some major summary reports."

Creating a laser that can melt a soda can in a lab is a finicky enough task. Later this year, scientists will put a 40,000-pound chemical laser in the belly of a gunship flying at 300 mph and take aim at targets as far away as five miles. And we're not talking aluminum cans. Boeing's new Advanced Tactical Laser will cook trucks, tanks, radio stations-the kinds of things hit with missiles and rockets today. Whereas conventional projectiles can lose sight of their target and be shot down or deflected, the ATL moves at the speed of light and can strike several targets in rapid succession.

Hortonworks, IBM and Red Hat today announced they're banding together to build a consistent hybrid computing architecture for big data workloads. Dubbed the Open Hybrid Architecture Initiative, the program pledges simplicity of deployment and freedom of movement for data apps. The rapid ascent of cloud computing platforms like AWS, Azure, and Google Cloud has given enterprises abundant new options for storing data and deploying processing-intensive applications, such as deep learning and real-time stream processing. Throw in the progress being made at the edge, with sensors and speedy ARM chips collecting and processing massive amounts of data, and you have the makings of a computing revolution. While the computing possibilities in the cloud and on the edge may appear bountiful, the reality is that the underlying architectures for building apps that can span these three modes are just starting to come together. Enterprises today face a dearth of repeatable patterns to guide their developers, administrators, and architects, who are tasked with building, deploying and maintaining hybrid that span not just the cloud and the edge, but traditional on-prem data centers too. Hybrid computing architecture for big data workloads https://goo.gl/GQVXjs

Hybrid computing architecture for big data workloads https://goo.gl/GQVXjs