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The next posts are merged in Scholar. Their mixed citations are counted only for the first article.
This "Cited by" count incorporates citations to the next articles or blog posts in Scholar. The ones marked * may be distinctive through the write-up during 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 economy is dealing with essentially the most challenging era of local weather modify further than what exactly is obvious inside the pre-industrial age. Although Africa's share of worldwide greenhouse gas (GHG) is nominal, the…
The next content articles are merged in Scholar. Their blended citations are counted only for the very first report.
Toward assault-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Finding out method
A not-for-gain organization, IEEE is the whole world's most significant technological Expert Group focused on advancing technologies for the advantage of humanity.
A QoE-driven multicast technique with segment routing—A novel multimedia site visitors engineering paradigm
The current period is struggling with a dilemma referring to participating the essentialities of Electricity methods to achieve economic prosperity on account of the ensuing environmental difficulties. For that reason, sectors…
An optimized quantum minimum seeking algorithm Jiahao Shen History Researcher with guaranteed-results chance and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do normal useful resource dependence, renewable Power, and technological innovations amplify or lessen the pollution noises?
Scanning radar forward-looking superresolution imaging determined by the Weibull distribution for just a sea-floor concentrate on
This "Cited by" depend contains citations to the next posts in Scholar. Those marked * could possibly be unique within the article 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。
Ways to disturb network reconnaissance: a transferring goal defense technique based upon deep reinforcement learning