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The following article content are merged in Scholar. Their merged citations are counted only for the first short article.
This "Cited by" rely includes citations to the subsequent content articles in Scholar. Those marked * could possibly be various within the report 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 experiencing by far the most demanding period of weather modify further than precisely what is evident from the pre-industrial age. Even though Africa's share of worldwide greenhouse gas (GHG) is small, the…
The following content are merged in Scholar. Their mixed citations are counted just for the very first short article.
Toward assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Finding out approach
A not-for-income organization, IEEE is the globe's largest specialized Experienced Business devoted to advancing technologies for the good thing about humanity.
A QoE-driven multicast approach with phase routing—A novel multimedia targeted traffic engineering paradigm
The current period is dealing with a Problem relating to participating the essentialities of Electricity resources to attain financial prosperity due to the following environmental difficulties. For that reason, sectors…
An optimized quantum minimal hunting algorithm with sure-success probability and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do pure useful resource dependence, renewable Electrical power, and technological innovations amplify or lessen the air pollution noises?
Scanning radar ahead-on the lookout superresolution imaging based upon the Weibull distribution for your sea-area concentrate on
This "Cited by" depend features citations to Jiahao Shen the subsequent articles in Scholar. Those marked * may be distinct with the article 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。
How to disturb network reconnaissance: a relocating focus on protection approach depending on deep reinforcement Mastering