Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

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...

You might also read

Related Articles

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

Sort by

Same author

Camptothecin Nanowires Induce the cGAS-STING Pathway to Remold Tumor-Associated Macrophages for Antitumor Immunity.

Pharmaceutics·2026

Same author

The multistep progression of areca nut-induced oral cancer: a mechanistic roadmap from pathogenesis to precision therapy.

Life medicine·2026

Same author

Assessing the Resilience of sEMG Classifiers to Sensor Malfunction and Signal Saturation.

Sensors (Basel, Switzerland)·2026

Same author

E-Selectin-Targeted Nanomicelles via Sialic Acid Conjugation for Anti-Inflammatory Efficacy and Alleviating the Progression of Metabolic-Associated Steatotic Liver Disease.

Biomaterials research·2026

Same author

From 1990 to 2021, the burden of diabetes among the elderly in the Western Pacific Region (WPR) and projections for 2040 prevalence: A systematic analysis of the 2021 Global Burden of Disease study.

Diabetes & metabolic syndrome·2025

Same author

Determining post-orthognathic surgery profile: A validation study on the potential of modified chin point as a reliable predictor.

PloS one·2025

Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026

Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026

Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026

Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026

Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026

Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 28, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

DGaze: CNN-Based Gaze Prediction in Dynamic Scenes.

Zhiming Hu, Sheng Li, Congyi Zhang

IEEE Transactions on Visualization and Computer Graphics

|February 20, 2020

Summary

This summary is machine-generated.

We developed DGaze, a novel CNN model for predicting user gaze in virtual reality. It analyzes head movement and scene saliency, improving real-time prediction accuracy by 22% in dynamic virtual environments.

More Related Videos

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: Dec 28, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Computer Vision
Human-Computer Interaction
Virtual Reality

Background:

Accurate gaze prediction is crucial for immersive virtual reality (VR) experiences, particularly in Head-Mounted Display (HMD) applications.
Understanding user gaze behavior in dynamic virtual scenes is essential for developing effective prediction models.
Existing methods often struggle with the complexities of dynamic environments and real-time prediction accuracy.

Purpose of the Study:

To analyze user gaze behaviors in dynamic virtual scenes and identify key correlating factors.
To propose a novel Convolutional Neural Network (CNN)-based model, DGaze, for enhanced gaze prediction in HMD-based applications.
To evaluate the performance of DGaze against prior methods and demonstrate its utility in real-world applications.

Main Methods:

Collected eye-tracking data from 43 users across 5 dynamic virtual scenes under free-viewing conditions.
Performed statistical analysis to identify correlations between gaze position and dynamic object positions, head rotation velocities, and salient regions.
Developed the DGaze CNN model, integrating object position sequences, head velocity sequences, and saliency features for gaze prediction.

Main Results:

DGaze achieved a 22.0% improvement in real-time gaze prediction accuracy over prior methods in dynamic scenes, using angular distance as the metric.
The model also showed a 9.5% improvement in static scenes.
A variant, DGaze_ET, demonstrated higher precision for future gaze prediction by incorporating eye-tracking data.

Conclusions:

Dynamic object positions, head rotation velocities, and salient regions are significant predictors of user gaze in virtual environments.
The proposed DGaze model offers superior performance for both real-time and future gaze prediction in HMD applications.
DGaze has practical applications in areas like gaze-contingent rendering and VR gaming, enhancing user experience and system efficiency.