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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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Related Experiment Video

Updated: Oct 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

676

Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction.

Kun Yu1, Xizhong Qin1, Zhenhong Jia1

  • 1College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces a novel Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) for accurate traffic flow prediction. The CAFMGCN model effectively captures dynamic spatio-temporal data diversity, outperforming existing methods.

Keywords:
cross-attentiondata diversityspatio-temporal multi-graphtraffic flow prediction

Related Experiment Videos

Last Updated: Oct 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

676

Area of Science:

  • Transportation Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Accurate traffic flow prediction is crucial for smart city development.
  • Existing models often fail to capture dynamic spatio-temporal data diversity due to static, local spatial dependencies.
  • The limitations of single-graph structures in current traffic prediction research necessitate advanced modeling approaches.

Purpose of the Study:

  • To propose a novel Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for enhanced traffic flow prediction.
  • To address the limitations of existing models in capturing dynamic spatio-temporal data diversity and complex spatial relationships.
  • To improve the accuracy and effectiveness of traffic flow prediction by integrating diverse temporal and spatial information.

Main Methods:

  • Utilized Graph Convolutional Networks (GCN) to model historical traffic data across three time attributes (current, daily, weekly) for feature extraction.
  • Constructed multiple dynamic spatial graphs (adjacency, connectivity, regional similarity) considering distance-traffic flow relationships.
  • Implemented a cross-attention mechanism to fuse extracted temporal and spatial features, leveraging global information to minimize prediction errors.

Main Results:

  • The proposed CAFMGCN model demonstrated superior accuracy in traffic flow prediction compared to baseline models.
  • Experimental evaluations confirmed the model's effectiveness in capturing complex spatio-temporal dependencies.
  • The integration of multi-graph structures and cross-attention significantly reduced prediction errors.

Conclusions:

  • The CAFMGCN model offers a more accurate and effective solution for traffic flow prediction in smart transportation systems.
  • The study highlights the importance of modeling dynamic spatio-temporal data diversity and complex spatial relationships for improved prediction.
  • The findings provide a valuable contribution to the field of intelligent transportation systems and urban planning.