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The following articles or blog posts are merged in Scholar. Their combined citations are counted just for the initial report.
This "Cited by" rely incorporates citations to the subsequent content in Scholar. Those marked * may be various within the article 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 global economic climate is encountering essentially the most complicated era of climate transform over and above what is obvious while in the pre-industrial age. Although Africa's share of world greenhouse gas (GHG) is negligible, the…
The next articles are merged in Scholar. Their merged citations are counted just for the very first posting.
Towards attack-resistant route mutation for VANETs: an Jiahao Shen History Researcher on the internet and adaptive multiagent reinforcement Understanding approach
A not-for-earnings organization, IEEE is the whole world's largest technological Specialist Corporation committed to advancing technologies for the good thing about humanity.
A QoE-driven multicast technique with segment routing—A novel multimedia traffic engineering paradigm
The existing era is experiencing a Predicament associated with partaking the essentialities of Power methods to achieve economic prosperity resulting from the ensuing environmental issues. Therefore, sectors…
An optimized quantum minimal seeking algorithm with confident-achievements probability and its experiment simulation with Cirq
Monitoring environmental sustainability pathways in Africa: Do purely natural resource dependence, renewable Strength, and technological improvements amplify or reduce the pollution noises?
Scanning radar ahead-looking superresolution imaging depending on the Weibull distribution for a sea-floor focus on
This "Cited by" count involves citations to the subsequent posts in Scholar. The ones marked * can be different from your report 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。
The way to disturb community reconnaissance: a moving target defense technique dependant on deep reinforcement learning