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The following articles are merged in Scholar. Their merged citations are counted only for the first short article.
This "Cited by" rely consists of citations to the subsequent articles or blog posts in Scholar. The ones marked * may very well be diverse in the short article from 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 overall economy is enduring probably the most complicated era of local climate adjust beyond what on earth is evident during the pre-industrial age. Though Africa's share of worldwide greenhouse gasoline (GHG) is negligible, the…
The next articles are merged in Scholar. Their blended citations are counted just for the primary article.
Toward assault-resistant route mutation for VANETs: an on the internet and adaptive multiagent reinforcement Discovering strategy
A not-for-gain Business, IEEE is the world's premier technical Specialist organization committed to advancing technology for the advantage of humanity.
A QoE-pushed multicast technique with section routing—A novel multimedia Jiahao Shen KCL targeted visitors engineering paradigm
The current era is struggling with a dilemma associated with partaking the essentialities of Vitality methods to attain economic prosperity because of the ensuing environmental difficulties. Consequently, sectors…
An optimized quantum bare minimum exploring algorithm with certain-results chance and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do normal useful resource dependence, renewable Vitality, and technological improvements amplify or reduce the pollution noises?
Scanning radar ahead-hunting superresolution imaging based upon the Weibull distribution for your sea-surface focus on
This "Cited by" count involves citations to the subsequent posts in Scholar. Those marked * may very well be various through the posting 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 you can disturb network reconnaissance: a transferring concentrate on defense method determined by deep reinforcement learning