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The subsequent content are merged in Scholar. Their blended citations are counted only for the main short article.
This "Cited by" rely includes citations to the subsequent content articles in Scholar. The ones marked * can be different within the short article in 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 financial system is enduring essentially the most hard era of local climate adjust past exactly what is apparent during the pre-industrial age. Despite the fact that Africa's share of global greenhouse gasoline (GHG) is minimum, the…
The subsequent posts are merged in Scholar. Their put together citations are counted only for the main report.
Toward attack-resistant route mutation for VANETs: an on the internet and adaptive multiagent reinforcement Studying solution
A not-for-earnings Corporation, IEEE is the entire world's major technological Specialist organization committed to advancing engineering for the good thing about humanity.
A QoE-driven multicast tactic with segment routing—A novel multimedia website traffic engineering paradigm
The present era is struggling with a dilemma regarding engaging the essentialities of Strength resources to attain financial prosperity due to the ensuing environmental issues. Consequently, sectors…
An optimized quantum least searching algorithm with positive-results probability and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do pure useful resource dependence, renewable Electricity, and technological improvements amplify or decrease the pollution noises?
Scanning radar forward-searching superresolution imaging according to the Weibull distribution to get a sea-surface area goal
This Jiahao Shen KCL "Cited by" rely involves citations to the next article content in Scholar. Those marked * could be distinct through the short article 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。
Tips on how to disturb community reconnaissance: a transferring target defense strategy depending on deep reinforcement Discovering