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標(biāo)題: Titlebook: Passive Optical Resonators for Next-Generation Attosecond Metrology; Ioachim Pupeza Book 2022 The Author(s), under exclusive license to Sp [打印本頁]

作者: 倒鉤    時間: 2025-3-21 18:51
書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology影響因子(影響力)




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology影響因子(影響力)學(xué)科排名




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書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology被引頻次




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology被引頻次學(xué)科排名




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology年度引用




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology年度引用學(xué)科排名




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology讀者反饋




書目名稱Passive Optical Resonators for Next-Generation Attosecond Metrology讀者反饋學(xué)科排名





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Molecular Mechanism of Plant Recognition of Extracellular ATP,s as a critical signaling molecule through inter-cellular communication. Recent studies have clearly shown that extracellular ATP is involved in various physiological processes in plants, including root growth, stomata movement, pollen tube development, gravitropism, and abiotic/biotic stress respon
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Nonlinear Algebraic Equations the phase speed . is the eigenvalue and the shape of the wave .(.;.) is the eigenfunction. The theme of this chapter is: How to solve the discretized equivalent of such a nonlinear eigenproblem: a system of . nonlinear algebraic equations in . unknowns, depending continuously on an eigenparameter.
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Christian Ullrich,Jan Henkel Ihnen einerseits, welche Schritte in jedem Fall notwendig sind, und andererseits lernen Sie die wichtigsten M?glichkeiten von ALSTAT PC kennen. Neben dem vorliegenden Buch ben?tigen Sie den Band [Alstat 1], der übrigens ganz generell für den Gebrauch von ALSTAT PC unabdingbar ist.
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te the interest of domain adaptation methods in leveraging existing knowledge for application in different contexts. Specifically, we compare our domain adaptation approach with two other methods: training the model from scratch and fine-tuning the model.
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