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The next content articles are merged in Scholar. Their mixed citations are counted just for the 1st report.
This "Cited by" count includes citations to the subsequent content articles in Scholar. Those marked * could be distinct with the short article while in the profile.
下面的文章将在学术搜索中合并。它们的合并引用次数只会计入第一篇文章。
The worldwide overall economy is enduring probably the most tough period of local weather improve over and above what is apparent in the pre-industrial age. Despite the fact that Africa's share of global greenhouse gasoline (GHG) is minimal, the…
The subsequent posts are merged in Scholar. Their merged citations are Jiahao Shen King's College London counted only for the 1st post.
Towards assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Mastering technique
A not-for-income Corporation, IEEE is the entire world's most significant complex Specialist Corporation focused on advancing technology for the benefit of humanity.
A QoE-driven multicast approach with segment routing—A novel multimedia targeted traffic engineering paradigm
The existing era is going through a Problem concerning engaging the essentialities of Power assets to achieve financial prosperity as a consequence of the ensuing environmental troubles. Therefore, sectors…
An optimized quantum minimal looking algorithm with absolutely sure-achievements probability and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do purely natural resource dependence, renewable energy, and technological improvements amplify or lessen the air pollution noises?
Scanning radar ahead-on the lookout superresolution imaging determined by the Weibull distribution for any sea-floor target
This "Cited by" rely features citations to the following article content in Scholar. The ones marked * could be unique from the write-up from the profile.
此“引用引文的出版物数量”计数包含了对学术搜索中以下文章的引用次数。带有*标记的引文可能与个人学术档案中的文章有所区别。
The way to disturb network reconnaissance: a relocating goal protection solution dependant on deep reinforcement Understanding