Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient? Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells. Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said.
How do you test out how AI will interact with physical systems in a real-world environment? If you're SparkCognition, an infrastructure-focused artificial intelligence (AI) company, you build a physical laboratory and testbed and let your engineers go wild. The company announced the opening of what it's calling HyperWerx, an autonomy facility that will help showcase the potential of AI integrated with physical systems and a proving grounds where SparkCognition and partners can throw some high tech spaghetti at the wall. These types of test beds have a long lineage in robotics labs like Willow Garage and with engineering playgrounds like Bell Labs. In a sector that's often thought of as software-first, this kind of embodied testing is an important step in bringing AI to the real world.
Autonomous vehicle technology developer Aurora Innovation Inc. said it plans to expand testing throughout Texas as it works toward commercializing self-driving trucks. In a May 27 blog post, the company said that it is expanding its relationships with shippers and motor carriers as it works to refine its autonomous Aurora Driver technology to fit their needs and handle highway traffic. To safely deploy a self-driving truck that can handle the complexities of highway driving, Aurora is developing and refining key capabilities such as complicated lane changes and merges, and entering and exiting the freeway. The goal is to create an autonomous freight system that is "safer, faster, more reliable and more efficient," the company said. Take a closer look at how we're preparing the Aurora Driver to move goods for key logistics companies on middle-mile routes in Texas https://t.co/FlvnmBNaCN Aurora said it takes about three days to deliver goods from Dallas to Los Angeles with humans at the wheel.
On May 25, 1961, President John F. Kennedy delivered a 46-minute speech that included historical context of the Cold War and how the US planned to triumph over the Soviets, but what won the hearts of the American people was his plan to send humans to the moon. 'Space is open to us now; and our eagerness to share its meaning is not governed by the efforts of others. We go into space because whatever mankind must undertake, free men must fully share...,' the late president said while standing behind the lectern during a joint session of Congress. 'First, I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to the earth.' The US had not even sent a human into orbit at the time of the speech, which placed it far behind the Soviets who had sent an astronaut to space a month before Kennedy addressed the nation.
Researchers at The University of Texas MD Anderson Cancer Center have developed a first-of-its-kind artificial intelligence (AI)-based tool that can accurately identify rare groups of biologically important cells from single-cell datasets, which often contain gene or protein expression data from thousands of cells. The research was published today in Nature Computational Science. This computational tool, called SCMER (Single-Cell Manifold presERving feature selection), can help researchers sort through the noise of complex datasets to study cells that would likely not be identifiable otherwise. SCMER may be used broadly for many applications in oncology and beyond, explained senior author Ken Chen, Ph.D., associate professor of Bioinformatics & Computational Biology, including the study of minimal residual disease, drug resistance and distinct populations of immune cells. "Modern techniques can generate lots of data, but it has become harder to determine which genes or proteins actually are important in those contexts," Chen said.
A powerful once-in-a-decade winter storm in February resulted in the near total collapse of Texas' power grid, resulting in residential and commercial areas suffering days-long blackouts, which led to at least 57 deaths and billions of dollars in property damage across the state's 254 counties. In addition, some Texans who did have power are facing overcharges of about $16 billion for electricity consumed during the weeklong crisis, according to a watchdog for the Electric Reliability Council of Texas (ERCOT), the quasi-governmental entity that oversees the Lone Star State's power grid. While debates as to the root causes of the grid's failure are likely to go on for months if not years, some energy experts contend that a potential solution exists that could have alleviated some of the worst effects of the power shutdown – the introduction of artificial intelligence (AI) into the management of the grid. Artificial Intelligence is loosely defined as the use of computer systems to process large volumes of data in order to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. Although AI technology has been embraced by a number of other economic sectors, such as retail and insurance industries, the operators of the U.S. power grid have been slower to adopt it.
The screen lights up with a new text message from Scott, a professor at the University of Texas: How many legs does a camel have? His smiley face seems to suggest that like other 13-year-olds, he's playful and maybe just a little bit childish. But in any case, it doesn't seem like his response is a serious attempt to answer the question. All business, Scott pointedly ignores the light-hearted response: How many legs does a millipede have? Again, Eugene doesn't give an accurate answer and seems to get a little distracted: Just two, but Chernobyl mutants may have up to five. I know you are supposed to trick me.
Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy's National Nuclear Security Administration. Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
New machine learning tools and enhanced natural language processing capabilities are among the latest additions to Oracle Analytics Cloud. Tech giant Oracle -- now based in Austin, Texas, after moving its headquarters recently from its longtime base in Redwood City, Calif. Oracle Analytics Cloud is the business intelligence piece of Oracle's analytics platform -- which also includes Oracle Analytics Server and Oracle Fusion Analytics Warehouse -- and is made up of Oracle's traditional BI reporting tools, self-service BI capabilities, data visualization tools and augmented intelligence capabilities. Before an overhaul in June 2019 designed to reduce complexity, Oracle's analytics platform consisted of 18 different products. New machine learning (ML) capabilities in Oracle Analytics Cloud include Machine Learning Explain-Ability, a tool that enables business users to view complete details of how machine learning models calculate predictability to get insight into influencing factors.