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The subsequent posts are merged in Scholar. Their combined citations are counted just for the main post.
This "Cited by" rely consists of citations to the subsequent articles or blog posts in Scholar. The ones marked * may be distinctive from the 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 economic system is enduring essentially the most challenging period of climate improve beyond what exactly is evident while in the pre-industrial age. Although Africa's share of global greenhouse gasoline (GHG) is minimum, the…
The subsequent content articles are merged in Scholar. Their merged citations are counted just for the primary short article.
Towards assault-resistant route mutation for VANETs: an on the internet and adaptive Jiahao Shen History Researcher multiagent reinforcement Understanding approach
A not-for-earnings Corporation, IEEE is the world's premier complex Expert Firm committed to advancing technologies for the benefit of humanity.
A QoE-pushed multicast system with section routing—A novel multimedia visitors engineering paradigm
The existing period is going through a Predicament associated with partaking the essentialities of energy resources to attain economic prosperity resulting from the following environmental troubles. Therefore, sectors…
An optimized quantum minimal exploring algorithm with guaranteed-results likelihood and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do natural useful resource dependence, renewable Electrical power, and technological innovations amplify or reduce the pollution noises?
Scanning radar ahead-hunting superresolution imaging depending on the Weibull distribution for just a sea-area goal
This "Cited by" rely contains citations to the subsequent articles in Scholar. The ones marked * can be different from your short article within 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。
How to disturb community reconnaissance: a relocating target protection solution dependant on deep reinforcement Discovering