Infraction 发表于 2025-3-25 05:45:18

W. Eisterer,W. Hilbe,H. Huber,J. Thalers showed how electromagnetic waves pass through space as vibrations in an all-pervasive ether..Maxwell began with a mechanical model of spinning spheres and vortices and ended with a model that linked quantities varying in space and time represented by abstract mathematical symbols. One of the few c

Stress 发表于 2025-3-25 08:15:34

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吼叫 发表于 2025-3-25 13:49:49

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即席演说 发表于 2025-3-25 16:09:27

Ádám Ignácztional scientists.Also available online on www.springermaterVapor-liquid equilibrium data, heat of mixing (excess enthalpies), and volumetric properties of systems are required for both the applications in design of industrial technological processes and the understanding the structure and the inter

流利圆滑 发表于 2025-3-25 23:26:45

J. Divya Navamani,A. Lavanya,Yash Vardhan Bhargava,Divya Kumar Gupta,Gaurav SinghavaScript by learning in detail how modern applications are made. In covering lesser-understood aspects of this powerful language and truly understanding how it works, your JavaScript code and programming skills will improve.  .You will learn about core fundamentals of JavaScript, including deep div

Gullible 发表于 2025-3-26 00:14:59

Gallardo Dayana,Guzmán Cristian,Barba Jessy,Valencia Jhomiraon and reflection.Compares the possibilities and limitations.This book, written by a pioneer in surface physics and thin film research and the inventor of Low Energy Electron Microscopy (LEEM), Spin-Polarized Low Energy Electron Microscopy (SPLEEM) and Spectroscopic Photo Emission and Low Energy Ele

Palate 发表于 2025-3-26 08:19:38

P. Stevens,B. Kriefmanktischen Unterricht der Histologie und mikro­ skopischen Anatomie für Studierende der Medizin und Zahnmedizin gedacht. Auch für den Arzt, für Biologen und für die Tätigkeit in histologischen Laboratorien kann das Buch von Interesse sein. Stoff­ inhalt und Gliederung haben sich im wesentlichen am Geg

眉毛 发表于 2025-3-26 10:49:34

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占卜者 发表于 2025-3-26 14:43:44

,COD: Learning Conditional Invariant Representation for Domain Adaptation Regression,) is developed for complex practical learning problems. However, due to the continuity problem in regression, existing conditional distribution alignment theory and methods with discrete prior, which are proven to be effective in classification settings, are no longer applicable. In this work, focus

Feckless 发表于 2025-3-26 16:53:28

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