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Related Concept Videos

End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

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Link Prediction of Green Patent Cooperation Network Based on Multidimensional Features.

Mingxuan Yang1, Xuedong Gao1, Yun Ye1

  • 1School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study introduces a novel multidimensional link prediction model for regional green patent cooperation networks. The model enhances prediction accuracy, aiding organizations in identifying potential technology collaboration partners.

Keywords:
green patentlink predictionmultidimensional featurespatent cooperation network

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Area of Science:

  • Innovation Studies
  • Network Science
  • Environmental Policy

Background:

  • Regional green patent cooperation networks are crucial for understanding collaborative innovation.
  • Link prediction in these networks can forecast trends and identify partners for technology collaboration.

Purpose of the Study:

  • To propose a multidimensional link prediction model for regional green patent cooperation networks.
  • To integrate node, path, and content features for improved prediction accuracy.
  • To apply the model to the Beijing-Tianjin-Hebei region for practical insights.

Main Methods:

  • Developed a model integrating node, path, and content features.
  • Utilized the entropy weight method for node similarity indicators.
  • Incorporated heterogeneous path analysis and patent text topic analysis for content similarity.
  • Employed the Grey Wolf Optimizer (GWO) for optimal weight determination.

Main Results:

  • The multidimensional prediction model significantly improves prediction accuracy compared to existing methods.
  • Experimental results validate the model's effectiveness in forecasting network evolution.
  • The model successfully predicted the green patent cooperation network in the Beijing-Tianjin-Hebei region.

Conclusions:

  • The proposed multidimensional link prediction model offers a robust approach for analyzing and forecasting green patent cooperation networks.
  • Accurate link prediction aids organizations in strategic partner identification for technological advancement.
  • The findings provide valuable insights into regional innovation dynamics and collaboration patterns.