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Titlebook: Der Bau der Starrluftschiffe; Ein Leitfaden für Ko Johannes Schwengler Book 1925 Springer-Verlag Berlin Heidelberg 1925 Konstrukteur.Luftsc

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21#
發(fā)表于 2025-3-25 05:34:58 | 只看該作者
22#
發(fā)表于 2025-3-25 07:56:46 | 只看該作者
Gemeinschaft in der Stadt — Die Gestaltung von Lebensverh?ltnissen als historische Aufgabe der Soziaine with the Sustainable Development Goals (SDGs)? We all know that ‘.’! We will need money to finance new, more environmentally friendly activities. This chapter shows how to involve civil society and more specifically citizens in the financing of this energy and sustainable transition, through the
23#
發(fā)表于 2025-3-25 11:56:23 | 只看該作者
Location Mention Detection in Tweets and Microblogstions of locations in the texts of microblogs and social media. We propose an approach based on Noun Phrase extraction and .-gram based matching instead of the traditional methods using Named Entity Recognition (NER) or Conditional Random Fields (CRF), arguing that our method is better suited to noi
24#
發(fā)表于 2025-3-25 17:16:09 | 只看該作者
25#
發(fā)表于 2025-3-25 21:18:00 | 只看該作者
Zermelo and the Axiomatic Methodd in his axiomatization of set theory. What is essential in that shared axiomatic method? And, exactly when was it established? By philosophical reflection on these questions, we are to uncover how Zermelo’s thought and Hilbert’s thought on the axiomatic method were developed interacting each other.
26#
發(fā)表于 2025-3-26 00:48:49 | 只看該作者
27#
發(fā)表于 2025-3-26 07:35:16 | 只看該作者
28#
發(fā)表于 2025-3-26 10:06:52 | 只看該作者
29#
發(fā)表于 2025-3-26 14:31:56 | 只看該作者
https://doi.org/10.1007/978-3-662-66815-3 our algorithm is compared with a reinforcement learning based on a traditional BP neural network using a boat problem. Simulation results show that the proposed algorithm is faster and more effective.
30#
發(fā)表于 2025-3-26 19:43:24 | 只看該作者
An Analysis of Machine Learning Algorithms for AQI Prediction,in different cities. We used various machine learning models such as linear regression, decision tree, random forest, and support vector regression to predict AQI values. The results show that machine learning models can be used to forecast AQI values with high accuracy.
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