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Titlebook: China-Handbuch für Manager; Kultur, Verhalten un Birgit Zinzius Book 2007 Springer-Verlag Berlin Heidelberg 2007 Asien.Auslandseinsatz.Chin

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發(fā)表于 2025-3-25 06:34:08 | 只看該作者
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A Multilevel Behavioral-Preventive School Programculties. The particular formulation underlying the multilevel behavioral-preventive school program to be discussed is that of Kelly (Mills & Kelly, 1972; Trickett, Kelly, & Todd, 1972). In essence, Kelly recognizes the inseparability of behavior from the many contexts in which it occurs (i.e., socia
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發(fā)表于 2025-3-25 16:04:56 | 只看該作者
AuDaLa is Turing Completections autonomously. Considering the paradigm and the design choices of AuDaLa, it is interesting to determine the expressiveness of the language and to create verification methods for it. In this paper, we take our first steps to such a verification method by implementing Turing machines in AuDaLa
25#
發(fā)表于 2025-3-25 22:53:57 | 只看該作者
The Unintended Interpretations of Intuitionistic Logic,e use of formal parts of language, in particular, first-order logic and Heyting Arithmetic. We include unintended interpretations of some mild variations on “official” intuitionism, such as intuitionistic type theories with full comprehension and higher order logic without choice principles or not s
26#
發(fā)表于 2025-3-26 04:11:53 | 只看該作者
The Impact of Artificial Intelligence on Portuguese Agricultureions are increasingly unstable, water resources are scarce, and diseases and pests are less controllable. This research work refers to different techniques for automating agricultural processes based on artificial intelligence (AI). This manuscript analyzes several research works with the objective
27#
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30#
發(fā)表于 2025-3-26 17:01:57 | 只看該作者
,ContainerGym: A Real-World Reinforcement Learning Benchmark for?Resource Allocation,that fits our resource allocation framework. We provide results of standard baseline methods. Going beyond the usual training reward curves, our results and the statistical tools used to interpret them allow to highlight interesting limitations of well-known deep reinforcement learning algorithms, namely PPO, TRPO and DQN.
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