Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
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.
For potentiometric titration, the Gran plot is created by plotting...
1.1K
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Processed meat intake and incident gout: Integrated evidence from epidemiology, plasma proteomics, and machine learning.

Journal of advanced research·2026
Same author

Piezoelectric nanofiber-based intelligent hearing system.

Science advances·2025
Same author

How government green fund reduce corporate carbon emissions.

Journal of environmental management·2025
Same author

How does the development of the digital economy influence carbon productivity? The moderating effect of environmental regulation.

Environmental science and pollution research international·2024
Same author

Corporate ESG performance when neighboring the Environmental Protection Agency.

Journal of environmental management·2023
Same author

New urbanization and carbon emissions intensity reduction: Mechanisms and spatial spillover effects.

The Science of the total environment·2023

Related Experiment Video

Updated: Jan 8, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K

Robust Traffic Forecasting With Disentangled Spatiotemporal Graph Neural Networks.

Ting Wang, Rui Luo, Daqian Shi

    IEEE Transactions on Neural Networks and Learning Systems
    |December 11, 2025
    PubMed
    Summary

    This study introduces disentangled spatiotemporal (DIST) graph neural networks for robust traffic forecasting. DIST improves prediction accuracy by decoupling stable traffic features from dynamic dependencies, addressing real-world distribution shifts.

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    14.1K

    Area of Science:

    • Intelligent Transportation Systems (ITSs)
    • Machine Learning
    • Graph Neural Networks

    Background:

    • Spatiotemporal graph neural networks (STGNNs) for traffic prediction often fail due to distribution shifts caused by external factors.
    • The assumption of independent and identically distributed (i.i.d.) traffic data is frequently violated in real-world scenarios.

    Purpose of the Study:

    • To develop a robust traffic forecasting framework resilient to distribution shifts.
    • To explicitly decouple invariant traffic features from dynamic spatiotemporal dependencies.

    Main Methods:

    • Proposed the disentangled spatiotemporal (DIST) graph neural networks framework.
    • Introduced a causality-driven learning objective to separate invariant variables from exogenous factors.
    • Developed a spatiotemporal graph modeling module and a graph perturbation module for adaptive dependency capture and topology variation simulation.

    Main Results:

    • The DIST framework learns topology-agnostic representations robust to distribution shifts.
    • The model successfully identifies and utilizes invariant features for improved traffic prediction accuracy.
    • Experiments on real-world data demonstrated the superiority of the proposed approach over existing methods.

    Conclusions:

    • The proposed DIST framework offers a robust solution for traffic forecasting in the presence of distribution shifts.
    • Explicitly disentangling invariant features enhances model resilience and prediction performance.
    • The methodology provides a pathway for developing more reliable intelligent transportation systems.