Researchers in renewable energy are applying deep learning (DL) to a variety of problems from diverse renewable energy domains, such as biofuels, wind, solar, power systems, …
As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on …
Addressing the so-called "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the actual energy consumption, as measured by …
Reliable Traffic State Estimation (TSE) is an important precursor to developing sophisticated traffic controls for intelligent transportation systems (ITS). Historically, TSE is …
This paper provides a system architecture for an infrastructure-based cooperative perception fusion engine for next-generation traffic control. This engine will provide a complete …
Crystallographic disorder is known to be crucial for the understanding of transport properties of intermetallic compounds. ZrSiS-type phases with the general formula ZnXSb, where X …
The investigation of the electronic properties of semiconductors from transport measurements (i.e., resistivity, Hall, and Seebeck coefficient measurements) is challenging due to …
New materials discovery is the driving force for progress in solid state physics and chemistry. Here we solve the crystal structure and comprehensively study physical properties of …
Zintl phases are excellent candidates for high-efficiency thermoelectrics (TEs) due to their extremely low lattice thermal conductivity. The manufacturing of an all-Zintl module is …