Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

752 indexed citations
published 2016

Countries where authors are citing Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

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This map shows the geographic impact of Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding more than expected).

Fields of papers citing Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding.

About Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

This paper, published in 2016, received 752 indexed citations . Written by Hong Lai, Jun Zhang, Lei Pan, Josef Pieprzyk, Fuyuan Xiao and Mehmet A. Orgun covering the research area of Artificial Intelligence and Atomic and Molecular Physics, and Optics. It is primarily cited by scholars working on Molecular Biology (170 citations), Materials Chemistry (130 citations) and Biomedical Engineering (96 citations). Published in Scientific Reports.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1038/srep31350.

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