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Titlebook: Union Wage Bargaining and Economic Growth; J?rg Lingens Book 2004 Springer-Verlag Berlin Heidelberg 2004 Economic Growth.Labour Markets.Un

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樓主: lumbar-puncture
31#
發(fā)表于 2025-3-26 23:05:04 | 只看該作者
The Growth Modelbent monopolist supplies the latest generation of the intermediate good to the final goods sector and finally the final goods sector, in which the consumption goods are produced using the ?state-of-the-art? intermediate good.
32#
發(fā)表于 2025-3-27 04:09:45 | 只看該作者
Nicolai sug- gested an analogous generalization for non-relativistic quantum mechanics. With the one-dimensional model introduced by Witten in 1981, supersym- metry became a major tool in quantum mechanics and mathematical, sta- tistical, and condensed-IIll;l. tter physics. Supersymmetry is also a
33#
發(fā)表于 2025-3-27 06:42:03 | 只看該作者
J?rg Lingensgs models in light of the discovery of the Higgs boson. The small value of the physical Higgs mass suggests that the Higgs quartic is likely loop generated; thus models with tree-level quartics will generically be more tuned.We classify the various models (including bona fide composite Higgs, little
34#
發(fā)表于 2025-3-27 09:26:17 | 只看該作者
35#
發(fā)表于 2025-3-27 14:58:51 | 只看該作者
J?rg LingensNoncommutative geometry has seen remarkable applications in high energy physics, viz. the geometrical interpretation of the Standard Model, however such a question has not been answered in a conclusive way so far..The book starts with a systematic analysis of the possibilities for so-called almost-c
36#
發(fā)表于 2025-3-27 20:05:43 | 只看該作者
37#
發(fā)表于 2025-3-28 00:25:19 | 只看該作者
thority—or even their existence—when we seek to understand them philosophically. Some philosophers would say that even this open-ended contrast between respect and skeptical doubt presupposes too much: ontological neutrality requires speaking not of moral properties but instead of moral . Nonetheles
38#
發(fā)表于 2025-3-28 04:05:04 | 只看該作者
39#
發(fā)表于 2025-3-28 06:37:18 | 只看該作者
40#
發(fā)表于 2025-3-28 13:05:00 | 只看該作者
J?rg Lingensin order to harvest potential runtime speedups known from quantum computing. This chapter will look into ‘genuine’ quantum models for machine learning which either have no direct equivalent in classical machine learning, or which are quantum extensions of classical models with a new quality of dynam
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