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

Special considerations while measuring oxygen saturation01:19

Special considerations while measuring oxygen saturation

622
Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
Ensuring accuracy in vital sign recordings while prioritizing patient comfort and minimizing anxiety is...
622
Time-Series Graph00:54

Time-Series Graph

4.4K
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...
4.4K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

393
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...
393
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

99
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
99
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

79
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
79

You might also read

Related Articles

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

Sort by
Same author

Nrf-2/HO-1 activation protects against oxidative stress and inflammation induced by metal welding fume UFPs in 16HBE cells.

Scientific reports·2024
Same author

A novel carbon price forecasting method based on model matching, adaptive decomposition, and reinforcement learning ensemble strategy.

Environmental science and pollution research international·2022
Same author

Infant Exposure to PCBs and PBDEs Revealed by Hair and Human Milk Analysis: Evaluation of Hair as an Alternative Biomatrix.

Environmental science & technology·2022
Same author

AQI time series prediction based on a hybrid data decomposition and echo state networks.

Environmental science and pollution research international·2021

Related Experiment Video

Updated: Jul 26, 2025

A Simple Approach to Manipulate Dissolved Oxygen for Animal Behavior Observations
06:20

A Simple Approach to Manipulate Dissolved Oxygen for Animal Behavior Observations

Published on: June 28, 2016

9.3K

A spatiotemporal dissolved oxygen prediction model based on graph attention networks suitable for missing data.

Yamin Fang1, Hui Liu2

  • 1Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, 410075, China.

Environmental Science and Pollution Research International
|June 19, 2023
PubMed
Summary

This study introduces a novel spatiotemporal prediction model for dissolved oxygen, effectively handling missing data using neural controlled differential equations (NCDEs) and graph attention networks (GATs). The model demonstrates superior accuracy in long-term water quality forecasting.

Keywords:
Dissolved oxygen predictionFusion attentionGraph attention networkMissing dataMulti-feature fusion

More Related Videos

A Model to Simulate Clinically Relevant Hypoxia in Humans
09:54

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

8.9K
Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System
16:41

Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System

Published on: November 5, 2013

16.2K

Related Experiment Videos

Last Updated: Jul 26, 2025

A Simple Approach to Manipulate Dissolved Oxygen for Animal Behavior Observations
06:20

A Simple Approach to Manipulate Dissolved Oxygen for Animal Behavior Observations

Published on: June 28, 2016

9.3K
A Model to Simulate Clinically Relevant Hypoxia in Humans
09:54

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

8.9K
Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System
16:41

Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System

Published on: November 5, 2013

16.2K

Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Data Science

Background:

  • Accurate dissolved oxygen (DO) prediction is vital for water pollution control.
  • Existing models struggle with missing data and capturing complex spatiotemporal dynamics.
  • Effective water resource management requires robust forecasting tools.

Purpose of the Study:

  • To develop a novel spatiotemporal prediction model for dissolved oxygen (DO) concentration.
  • To address the challenge of missing data in water quality time series.
  • To improve the accuracy and robustness of DO prediction models.

Main Methods:

  • Utilized neural controlled differential equations (NCDEs) for effective handling of missing data.
  • Employed graph attention networks (GATs) to capture complex spatiotemporal relationships in DO data.
  • Enhanced model performance through iterative optimization, feature selection (SHAP), and a fusion graph attention mechanism.

Main Results:

  • The proposed model demonstrated superior long-term prediction accuracy (step=18) compared to other models.
  • Achieved a Mean Absolute Error (MAE) of 0.194, Nash-Sutcliffe Efficiency (NSE) of 0.914, Relative Absolute Error (RAE) of 0.219, and Index of Agreement (IA) of 0.977.
  • Validated using water quality data from Hunan Province, China (Jan 2021 - Jun 2022).

Conclusions:

  • Constructing appropriate spatial dependencies significantly enhances DO prediction accuracy.
  • The NCDE module provides robustness to missing data, a common issue in environmental monitoring.
  • The developed model offers a reliable tool for water quality management and pollution control.