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Norfolk State University has announced that Professor Messaoud Bahoura, who heads the Center for Materials Research, and his team, have recently published a peer-reviewed research article in the Springer Journal of Materials Science: Materials in Electronics.
The group at Norfolk State is working on developing new materials to enhance technology and reduce energy consumption.
As current technology has reached its size limits, scientists are focusing on finding better-performing materials.
These advanced materials exhibit potential with lower energy leakage and higher efficiency when used in electronic devices.
Bahoura's group has made promising progress with minimal leakage currents, suggesting the potential for improved and more efficient electronic devices in the future.
Meanwhile, a research team at the College of Science and Technology at North Carolina Agricultural and Technical State University has conducted a pilot project using models available through Microsoft's Azure Open AI Service to develop a system that is expected to result in advancements in traffic management.
The pilot project, led by principal investigator Dr. Leila Hashemi-Beni and doctoral student Tewodros Syum Gebre, received support from Microsoft under the "Accelerating Foundation Models Research" (AFMR) collaboration.
The AFMR program provides academic researchers with access to state-of-the-art foundation models through Azure AI Services, aiming to foster a global AI research community and create robust, trustworthy models that can further research in various domains, including scientific discovery, education, healthcare, multicultural empowerment, legal work, and design.
This initiative involves 200 projects at universities in 15 countries, covering a wide range of focus areas.
The researchers at N.C. A&T aimed to utilize their expertise to enhance current monitoring systems as traffic congestion leads to increased emissions.
Hashemi-Beni, an associate professor in the Department of Built Environment, with research interests in remote sensing and geospatial data science and their applications, including transportation planning, expressed that current traffic management requires significant manual effort, which can be quite limiting.