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Titlebook: Web Dynpro ABAP for Practitioners; Ulrich Gellert,Ana Daniela Cristea Book 2013Latest edition Springer-Verlag Berlin Heidelberg 2013 ABAP.

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樓主: 爆發(fā)
21#
發(fā)表于 2025-3-25 06:39:55 | 只看該作者
Ulrich Gellert,Ana Daniela Cristeay material culture are not more widely questioned. Yet even among the most sympathetic historians of ecological ideas, the validity of an utterly materialistic worldview remains unchallenged. Karl Kroeber, to take a recent example, suggests that “the Romantic era is especially interesting because in
22#
發(fā)表于 2025-3-25 08:58:24 | 只看該作者
Ulrich Gellert,Ana Daniela Cristeararchy, Fordist in its obsession with gaining economies of scale and Taylorist in its surveillance of performance criteria. It has assumed a production and output logic derived from manufacturing, whereas both private and public services differ from this. Nonetheless there recently has been a resurg
23#
發(fā)表于 2025-3-25 15:11:44 | 只看該作者
24#
發(fā)表于 2025-3-25 19:11:22 | 只看該作者
25#
發(fā)表于 2025-3-25 20:30:03 | 只看該作者
Ulrich Gellert,Ana Daniela Cristea the energy error in powers of the mesh-width h. In goal-oriented refinement where the focus is on minimizing the error in certain functionals the adaptive refinement is steered by two errors, the error in the primal, the original, problem and the error in the dual problem, the approximation of the
26#
發(fā)表于 2025-3-26 04:01:32 | 只看該作者
Ulrich Gellert,Ana Daniela Cristeacessfully extended to time series classification in recent years. As a generalization of the nearest subspace classifier, the performance of SRC depends on a rich set of training samples for each class, which can span as many variations of each class as possible under testing conditions. However, du
27#
發(fā)表于 2025-3-26 04:36:11 | 只看該作者
28#
發(fā)表于 2025-3-26 10:31:13 | 只看該作者
29#
發(fā)表于 2025-3-26 16:04:44 | 只看該作者
30#
發(fā)表于 2025-3-26 20:28:30 | 只看該作者
Ulrich Gellert,Ana Daniela Cristearely on a large number of independent identically distributed samples. However, the complexity and variability of coal-rock deposit states make the dataset exhibit small sample characteristics, resulting in poor performance of deep learning model. To address this problem, this paper proposes a frame
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