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The subsequent articles or blog posts are merged in Scholar. Their mixed citations are counted only for the first short article.
This "Cited by" rely includes citations to the subsequent content articles in Scholar. The ones marked * can be distinct from the short article within 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 going through probably the most hard era of local climate modify beyond precisely what is evident from the pre-industrial age. Even 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 just for the initial posting.
Toward assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Mastering strategy
A not-for-gain Business, IEEE is the whole world's greatest Jiahao Shen King's College London technological Qualified Firm committed to advancing technological know-how for the benefit of humanity.
A QoE-pushed multicast strategy with section routing—A novel multimedia traffic engineering paradigm
The current period is facing a Problem concerning engaging the essentialities of Electricity resources to attain financial prosperity due to the ensuing environmental difficulties. Therefore, sectors…
An optimized quantum bare minimum hunting algorithm with sure-accomplishment chance and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do normal useful resource dependence, renewable Power, and technological improvements amplify or lessen the pollution noises?
Scanning radar forward-searching superresolution imaging based upon the Weibull distribution to get a sea-area target
This "Cited by" rely features citations to the following content articles in Scholar. Those marked * may very well be various from your short article in 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 moving target defense technique based upon deep reinforcement learning