![Quantum—Squeezed light cuts noise](/sites/default/files/styles/list_page_thumbnail/public/2019-06/Quantum-Squeezed_light_cuts_noise_0.jpg?h=557ecedc&itok=dbeUQ4mY)
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![Quantum—Squeezed light cuts noise](/sites/default/files/styles/list_page_thumbnail/public/2019-06/Quantum-Squeezed_light_cuts_noise_0.jpg?h=557ecedc&itok=dbeUQ4mY)
![Computing—Routing out the bugs](/sites/default/files/styles/list_page_thumbnail/public/2019-11/VA-HealthIT-2019-P04263.jpg?h=784bd909&itok=uwv091uK)
A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool
![Using artificial intelligence, Oak Ridge National Laboratory analyzed data from published medical studies to reveal the potential of direct and indirect impacts of bullying.](/sites/default/files/styles/list_page_thumbnail/public/2019-04/bullying_img.png?h=48484608&itok=zxX54Jz1)
Oak Ridge National Laboratory is using artificial intelligence to analyze data from published medical studies associated with bullying to reveal the potential of broader impacts, such as mental illness or disease.
![Low-cost, compact, printed sensor that can collect and transmit data on electrical appliances for better load monitoring](/sites/default/files/styles/list_page_thumbnail/public/2019-03/2019-P01301_0.jpg?h=c6980913&itok=y0S4bq0p)
Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.
![Molecular dynamics simulations of the Fs-peptide revealed the presence of at least eight distinct intermediate stages during the process of protein folding. The image depicts a fully folded helix (1), various transitional forms (2–8), and one misfolded state (9). By studying these protein folding pathways, scientists hope to identify underlying factors that affect human health.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Slide1_0.png?h=c855054e&itok=aNbgxXsc)
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data.
![ORNL helps identify challenges of extremely heterogeneous architectures](/sites/default/files/styles/list_page_thumbnail/public/2019-03/2019-P00928.jpg?h=036a71b7&itok=jpPwkNMg)
Three ORNL computer scientists headed the 2018 DOE ASCR Basic Research Needs Workshop on Extreme Heterogeneity and development of its final report.
![A LandScan image of population distributions. The ORNL-developed LandScan/LandCast Population Datasets and the Quantum Random Number Generator received national Excellence in Technology Transfer awards from the Federal Laboratory Consortium.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/LandScanFLC200_3.png?h=31ae35b7&itok=tWG4Qzy0)
OAK RIDGE, Tenn., March 13, 2019 – Two technologies from the Department of Energy’s Oak Ridge National Laboratory have received national Excellence in Technology Transfer awards from the Federal Laboratory Consortium for Technology Transfer: “Qrypt Lice
![(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/TourassiWH%5B1%5D.png?h=26b5064d&itok=HUC2iYmE)
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
OAK RIDGE, Tenn., Feb.
![Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w](/sites/default/files/styles/list_page_thumbnail/public/rs2019_highlight_plot_3d.png?itok=5bROV_ys)
A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.