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Titlebook: Advanced Materials for Sustainable Energy and Engineering; Selected Proceedings El Mehdi Elkhattabi,Mourad Boutahir,Abdelhai Rahma Conferen

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發(fā)表于 2025-3-21 17:01:58 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Advanced Materials for Sustainable Energy and Engineering
期刊簡稱Selected Proceedings
影響因子2023El Mehdi Elkhattabi,Mourad Boutahir,Abdelhai Rahma
視頻videohttp://file.papertrans.cn/168/167170/167170.mp4
發(fā)行地址Explores various approaches and methods to study novel nanomaterials (graphene, CNTs, phosphorene….).Provides up-to-day discussions of nanomaterials (types, characteristics, and properties).Covers the
學(xué)科分類Springer Proceedings in Energy
圖書封面Titlebook: Advanced Materials for Sustainable Energy and Engineering; Selected Proceedings El Mehdi Elkhattabi,Mourad Boutahir,Abdelhai Rahma Conferen
影響因子.This book presents selected peer-reviewed proceedings from the International Conference on Advanced Materials, Sustainable Energy, and Engineering (ICAMSEE2023), held at Ecole Normale Supérieure, University Moulay Ismail Meknes, Morocco, from November 27 to 29, 2023. The conference served as an exceptional platform for international and national scientists, professors, students, and industry professionals to convene and exchange knowledge in the fields of materials science, microscopy, engineering, technology, and energy. The book features contributions from researchers and experts, including keynote speakers, special sessions, posters, and tutorials, showcasing the latest advancements and developments in these areas of research..The topics covered in this book span a wide array of subjects within the realm of advanced materials, sustainable energy, and engineering. The forefront of materials science is explored, including nanomaterials, carbon nanotubes, graphene,materials for various applications, environmental protection, advanced optical materials, thermoelectric and magnetic materials, and additive manufacturing. Addressing the energy demands of today, the focus extends to no
Pindex Conference proceedings 2024
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Grundlagen der NetzwerkforschungExperiments were carried out adopting advanced Language Models to construct an encoding of the tweet to create a potential input vector on top of BERT and its variants. By employing Transformer-based models, we achieve significant improvements in bot detection accuracy compared to traditional method
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