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Titlebook: Neural-Symbolic Cognitive Reasoning; Artur S. d’Avila Garcez,Luís C. Lamb,Dov M. Gabbay Textbook 2009 Springer-Verlag Berlin Heidelberg 20

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發(fā)表于 2025-3-23 11:27:39 | 只看該作者
tion, novel separation techniques with liquid membrane, microemulsion and reversed micelles are also presented. This book will introduce the biochemical engineer and process engineer to the recovery of products from complex cultivation broths by modern techniques of solvent extraction and help them with proce978-3-642-08190-3978-3-662-03064-6
12#
發(fā)表于 2025-3-23 15:48:32 | 只看該作者
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發(fā)表于 2025-3-23 19:16:54 | 只看該作者
l., 1998; Strickley, 1999, 2000). Further constraints are imparted by the small volume of administration appropriate for subcutaneous and intramuscular delivery routes which need to be consistent with patient compliance and ease of delivery. This can be very different from the volume/ concentration
14#
發(fā)表于 2025-3-23 23:58:57 | 只看該作者
er the experi- mental results nor their interpretation contradict the proposed model; the experimental results do not contradict the proposed model but their interpretation by the authors does; finally, both the experimental results and their interpretation may be found to be incompatible with the f
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發(fā)表于 2025-3-24 04:56:47 | 只看該作者
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發(fā)表于 2025-3-24 08:43:01 | 只看該作者
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發(fā)表于 2025-3-24 10:51:15 | 只看該作者
18#
發(fā)表于 2025-3-24 15:56:58 | 只看該作者
?elds where algebraic computational problems might be encountered. In Engineering for example, network densi?cation and robotics apply resection and intersection techniques which require - gebraic solutions. Solution of nonlinear systems of equations is an indispensable task in almost all geoscience
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發(fā)表于 2025-3-24 20:55:45 | 只看該作者
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發(fā)表于 2025-3-24 23:09:17 | 只看該作者
Connectionist Temporal Reasoning,tion of the . (CTLK) system by applying it to a distributed time and knowledge representation problem, the full version of the . [87]. We also describe experiments on learning in the muddy children puzzle, showing how knowledge evolution can be analysed and understood in a learning model.
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