According to a new study in the journal Nature Materials, researchers from Stanford University have harnessed the power of machine learning technology to reverse long-held suppositions about the way lithium-ion batteries charge and discharge, providing engineers with a new list of criteria for making longer-lasting battery cells. This is the first time machine learning has been coupled with knowledge obtained from experiments and physics equations to uncover and describe how lithium-ion batteries degrade over their lifetime. Machine learning accelerates analyses by finding patterns in large amounts of data. In this instance, researchers taught the machine to study the physics of a battery failure mechanism to design superior and safer fast-charging battery packs. Fast charging can be stressful and harmful to lithium-ion batteries, and resolving this problem is vital to the fight against climate change.
Autonomous driving, connectivity, car sharing, electric vehicles, and the rise of renewable energy will all have powerful mutually reinforcing effects. For example, the introduction of self-driving cars in the 2020s will increase the use of EVs in high-use services such as ride-hailing because lower operating costs will offset the higher initial costs of these vehicles. The movement of people and goods is central to our society and economic activities. According to a BNEF-McKinsey & Company study, the change in how people move around cities will put the automotive and energy industries, as well as governments, under pressure. Light-duty vehicle fuel consumption could drop by up to 75% in some cities by 2030, prompting governments to look for new ways to recoup lost fuel taxes.
The research team led by Prof. Zhang Jie from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences made progress on real-time determination of earthquake focal mechanisms through deep learning. The work was published in Nature Communications. Since there are connections between characteristics of the rupture surface of the source fault and seismic wave radiated by the source, it's vital to monitor the earthquake by immediate determination of the source focal mechanism which is inferred from multiple ground seismic records. However, it's hard to calculate the mechanism from the simple records. The parameters about focal mechanisms are either merely reported or reported after a few minutes or even longer.
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.
"LG's strategic decision to exit the incredibly competitive mobile phone sector will enable the company to focus resources in growth areas such as electric vehicle components, connected devices, smart homes, robotics, artificial intelligence and business-to-business solutions, as well as platforms and services," it said in a statement.
At the center of the growing digital economy is data. Data is to the 21st century what oil was to the 20th century. In every industry, it are the companies that can use data effectively that succeed. And investing is no different. In their search for alpha generating ideas, investment managers are increasingly turning to sources of alternative financial data. But what is alternative data and how does it give fund managers an edge? The returns generated by investors can be classified as either alpha or beta.
In a September 2020 essay in Nature Energy, three scientists posed several "grand challenges" -- one of which was to find suitable materials for thermal energy storage devices that could be used in concert with solar energy systems. Fortuitously, Mingda Li -- the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering at MIT, who heads the department's Quantum Matter Group -- was already thinking along similar lines. In fact, Li and nine collaborators (from MIT, Lawrence Berkeley National Laboratory, and Argonne National Laboratory) were developing a new methodology, involving a novel machine-learning approach, that would make it faster and easier to identify materials with favorable properties for thermal energy storage and other uses. The results of their investigation appear this month in a paper for Advanced Science. "This is a revolutionary approach that promises to accelerate the design of new functional materials," comments physicist Jaime Fernandez-Baca, a distinguished staff member at Oak Ridge National Laboratory.
AI and ML applications will help your business improve efficiency. They will allow you to abandon routine tasks that slow down the processes in your company. AI and ML solutions will help automate algorithms and processes in your company, which will lead to cost savings and increased profits. Using AI applications, you can focus on more important tasks. Where else if not in the energy industry, AI has found a use.
Air pollution from the burning of fossil fuels impacts human health but predicting pollution levels at a given time and place remains challenging, according to a team of scientists who are turning to deep learning to improve air quality estimates. Results of the team's study could be helpful for modelers examining how economic factors like industrial productivity and health factors like hospitalizations change with pollution levels. "Air quality is one of the major issues within an urban area that affects people's lives," said Manzhu Yu, assistant professor of geography at Penn State. "Yet existing observations are not adequate to provide comprehensive information that may help vulnerable populations to plan ahead." Satellite and ground-based observations each measure air pollution, but they are limited, the scientists said.