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Titlebook: überempfindlichkeit und Immunit?t; Reaktionen Zweiter T E. E. Fischel,E. Letterer,L. Zicha Book 1967 Springer-Verlag, Berlin · Heidelberg 1

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21#
發(fā)表于 2025-3-25 04:40:38 | 只看該作者
22#
發(fā)表于 2025-3-25 07:55:11 | 只看該作者
23#
發(fā)表于 2025-3-25 15:02:01 | 只看該作者
überempfindlichkeit und Immunit?t978-3-642-88548-8Series ISSN 0085-1388
24#
發(fā)表于 2025-3-25 16:53:05 | 只看該作者
25#
發(fā)表于 2025-3-25 21:36:33 | 只看該作者
The Interacting Bose Fluid: Path Integral Representations and Renormalization Group Approachvaluation of such path integrals are discussed: the saddle point method and the method of the renorma-lization group. These methods are applied to the critical behavior of the interacting Bose fluid. The saddle point method leads naturally to a description of the à transition in terms of quantized v
26#
發(fā)表于 2025-3-26 01:14:23 | 只看該作者
27#
發(fā)表于 2025-3-26 05:01:05 | 只看該作者
The Rebellion of December 1851: the Death Throes of the Red Republican Cause, coup d’état carried out in Paris on the night of 1–2 December by the Prince-President, in a bid to seize personal power before the elections of 1852 might reject him. Men and women of the radical left were roused to resistance by the coup, but not as rapidly or spontaneously as in June 1848. The Ju
28#
發(fā)表于 2025-3-26 09:29:55 | 只看該作者
29#
發(fā)表于 2025-3-26 13:27:58 | 只看該作者
Information Logistics in Engineering Change Management: Integrating Demand Patterns and Recommendatinge management processes. This paper aims at extending this work by investigating approaches from group recommendation systems for implementing IT-support of the pattern use. The paper presents an approach for integration information demand patterns and recommendation systems, an architecture for recommendation systems and a clustering approach.
30#
發(fā)表于 2025-3-26 20:29:53 | 只看該作者
Research on K-Means Clustering Algorithm Over Encrypted Data-Means algorithm is too dependent on the initial value. In this paper, we focus on solving the problem to reduce the number of iterations, and improve the clustering efficiency. The experimental results demonstrate that our proposed, HK-Means algorithm has good clustering performance and the running time is also reduced.
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