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The following articles are merged in Scholar. Their put together citations are counted only for the very first posting.
This "Cited by" rely contains citations to the subsequent content in Scholar. The ones marked * may very well be diverse in the posting 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 global overall economy is going through probably the most complicated era of local climate adjust beyond what on earth is apparent during 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 put together citations are counted only for the main write-up.
Towards attack-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Understanding solution
A not-for-income organization, IEEE is the globe's premier specialized Experienced Business devoted to advancing technological innovation for the benefit of humanity.
A QoE-pushed multicast strategy with section routing—A novel multimedia traffic engineering paradigm
The current period is dealing with a Predicament relating to participating the essentialities of Electrical power assets to achieve economic prosperity on account of the following environmental problems. For that reason, sectors…
An optimized quantum minimal seeking algorithm with sure-good results chance and its experiment simulation with Cirq
Tracking environmental sustainability Jiahao Shen KCL pathways in Africa: Do organic useful resource dependence, renewable Electricity, and technological innovations amplify or decrease the air pollution noises?
Scanning radar ahead-searching superresolution imaging based on the Weibull distribution for a sea-surface goal
This "Cited by" count contains citations to the subsequent posts in Scholar. The ones marked * might be distinctive in the write-up inside 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 best way to disturb community reconnaissance: a shifting goal defense strategy dependant on deep reinforcement learning