The Sultanate of Oman continues to emphasise the importance of knowledge and cultural development with the launch of the 4th edition of the Excellence Competition for Private Libraries and Cultural Centres by the Ministry of Culture, Sports, and Youth.
According to reports, this initiative aims to promote smart private libraries and enhance their role in fostering intellectual growth under the theme Open Knowledge Horizons.
The competition holds special significance as it coincides with the celebration of Arab Libraries Day on March 10, reinforcing Oman’s commitment to strengthening the cultural and literary landscape.
By encouraging the active participation of private libraries and cultural centers, the initiative seeks to elevate their services, ensuring they remain vital pillars of societal progress.
Read More: Omani students create AI drone system for solar panel inspection
This year, the competition will assess the effectiveness of libraries and cultural centers across various governorates in Oman, focusing on their ability to adapt to modern developments in information management and cultural engagement.
By supporting these institutions, the competition aims to enhance their contributions to the community, inspiring a spirit of innovation and excellence.
The initiative also encourages libraries and cultural centers across Oman to expand their outreach, improve their services, and play a more dynamic role in shaping an informed and culturally enriched society.
Earlier, a group of students from Oman’s University of Technology and Applied Sciences in Shinas have pioneered an advanced AI-driven drone solar panel inspection system.
This AI-driven drone system allows for efficient identification and resolution of defects, winning the team multiple respected awards and offering their work at key scientific forums, local media outlets reported.
Team member Fatma bint Mohammed Al-Maamari explained that the project aims to address defects in solar panels caused by environmental, electrical, and mechanical factors.
The drone, equipped with a thermal camera, gathers data that is analyzed using advanced technologies such as a “Deep Learning Mod.”
The initiative underwent several phases, including research, data collection, and evaluation, all focused on enhancing detection accuracy and reducing processing time.
Advanced image processing techniques were employed to classify solar panel conditions and optimize analysis.
Another team member, Mahra bint Saeed Al-Kaabi, highlighted the extensive testing of the prototype on solar panel inspection at the university.
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