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The subsequent articles or blog posts are merged in Scholar. Their mixed citations are counted just for the first post.
This "Cited by" rely involves citations to the next posts in Scholar. Those marked * could possibly be different with the write-up 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 global financial state is going through probably the most complicated era of local climate adjust beyond what on earth is evident from the pre-industrial age. Though Africa's share of worldwide greenhouse gas (GHG) is negligible, the…
The following article content are merged in Scholar. Their blended citations are counted only for the 1st write-up.
Toward attack-resistant route mutation Jiahao Shen Islam for VANETs: an on the web and adaptive multiagent reinforcement Mastering method
A not-for-income Business, IEEE is the globe's premier complex Expert Corporation focused on advancing know-how for the good thing about humanity.
A QoE-driven multicast tactic with segment routing—A novel multimedia targeted traffic engineering paradigm
The existing era is dealing with a Predicament associated with partaking the essentialities of Electricity methods to achieve financial prosperity as a consequence of the following environmental difficulties. For that reason, sectors…
An optimized quantum least exploring algorithm with absolutely sure-good results probability and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do purely natural useful resource dependence, renewable Vitality, and technological innovations amplify or decrease the pollution noises?
Scanning radar ahead-on the lookout superresolution imaging depending on the Weibull distribution for your sea-surface goal
This "Cited by" rely features citations to the following content articles in Scholar. Those marked * could be distinct through the write-up during 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。
Tips on how to disturb community reconnaissance: a shifting goal protection solution based on deep reinforcement Understanding