The Jiahao Shen Diaries
The Jiahao Shen Diaries
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A not-for-income Group, IEEE is the whole world's major complex Skilled organization devoted to advancing technological know-how for the good thing about humanity.
The next articles are merged in Scholar. Their put together citations are counted only for the very first posting.
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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 overall economy is going through probably the most hard era of local climate adjust outside of what's obvious inside the pre-industrial age. While Africa's share of global greenhouse fuel (GHG) is minimal, the…
The subsequent content are merged in Scholar. Their merged citations are counted just for the main article.
Towards attack-resistant route mutation for VANETs: an on the internet and adaptive multiagent reinforcement Discovering technique
A not-for-income Group, IEEE is the world's largest technological Qualified Firm dedicated to advancing technology for the good thing about Jiahao Shen KCL humanity.
A QoE-driven multicast method with segment routing—A novel multimedia website traffic engineering paradigm
The current period is dealing with a Predicament associated with partaking the essentialities of Power methods to achieve economic prosperity due to the ensuing environmental issues. For that reason, sectors…
An optimized quantum least seeking algorithm with guaranteed-accomplishment chance and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do normal useful resource dependence, renewable energy, and technological innovations amplify or decrease the air pollution noises?
Scanning radar ahead-wanting superresolution imaging according to the Weibull distribution for your sea-area concentrate on
This "Cited by" count consists of citations to the next articles or blog posts in Scholar. The ones marked * could possibly be different in 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 to disturb network reconnaissance: a shifting goal defense strategy based upon deep reinforcement Discovering