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Second Uniqueness Theorem

Consider a region consisting of several individual conductors with a definite charge density in the region between these conductors. The second uniqueness theorem states that if the total charge on each conductor and the charge density in the in-between region are known, then the electric field can be uniquely determined.
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Related Experiment Video

Updated: Jun 13, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Identifying duplicate content using statistically improbable phrases.

Mounir Errami1, Zhaohui Sun, Angela C George

  • 1Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu

Bioinformatics (Oxford, England)
|May 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using statistically improbable phrases (SIPs) to detect duplicate scientific content. The SIP discovery method significantly enhances the accuracy of identifying duplicated citations in literature databases.

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Area of Science:

  • Bibliometrics
  • Information Science
  • Computational Linguistics

Background:

  • Document similarity metrics are used to identify similar topics and detect plagiarism in scientific literature.
  • Previous methods were limited to comparing abstracts due to computational intensity.
  • Extending comparisons to full text via online search engines offers greater potential.

Purpose of the Study:

  • To develop and evaluate a novel method for detecting duplicate or plagiarized scientific content.
  • To overcome the limitations of CPU-intensive comparisons by utilizing statistically improbable phrases (SIPs).

Main Methods:

  • A new method analyzing statistically improbable phrases (SIPs) was developed.
  • The method was applied to MEDLINE citations for duplicate content detection.
  • Direct quote searches were optimized for large-scale programmatic analysis.

Main Results:

  • The SIP discovery method demonstrated improved performance in detecting duplicate citations compared to existing algorithms.
  • Achieved a precision of 78.9% and recall of 99.6% for duplicate citation detection.
  • Outperformed the eTBLAST algorithm in precision (78.9% vs 50.3%).

Conclusions:

  • The SIP discovery method offers a more effective approach to identifying duplicate scientific content.
  • This technique enhances the integrity of literature reference databases.
  • Identified similar citations are available in the Déjà vu database.