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The next articles or blog posts are merged in Scholar. Their mixed citations are counted only for the first posting.

This "Cited by" depend involves citations to the following articles in Scholar. Those marked * could be unique from the posting 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 economy is encountering the most complicated era of climate adjust past exactly what is apparent in the pre-industrial age. While Africa's share of world greenhouse fuel (GHG) is negligible, the…

The subsequent content are merged in Scholar. Their put together citations are counted only for the initial report.

Towards attack-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Understanding approach

A not-for-earnings Group, IEEE is the entire world's most significant complex professional Firm dedicated to advancing technological innovation for the benefit of humanity.

A QoE-pushed multicast method with section routing—A novel multimedia targeted visitors engineering paradigm

The existing era is facing a Problem relating to partaking the essentialities of Power methods to attain financial prosperity as a result of the ensuing environmental troubles. As a result, sectors…

An optimized quantum least looking algorithm with confident-success probability and its experiment simulation with Cirq

Tracking environmental sustainability pathways in Africa: Do all-natural source dependence, renewable Electrical power, and technological innovations amplify or decrease the pollution noises?

Scanning radar forward-looking Jiahao Shen King's College London superresolution imaging based on the Weibull distribution for any sea-surface area concentrate on

This "Cited by" count contains citations to the subsequent posts in Scholar. Those marked * may very well be various within the report while in 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 best way to disturb network reconnaissance: a relocating concentrate on protection method dependant on deep reinforcement learning