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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

55
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
55
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

447
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
447

You might also read

Related Articles

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

Sort by
Same author

Microplastic trafficking in maternal-fetal system: a systematic review and quantitative profiling linking particle characteristics to developmental biology.

Critical reviews in toxicology·2026
Same author

Data-driven design of hybrid graphene oxide-MXene polymeric membranes for water purification.

iScience·2026
Same author

Sulfuric acid-induced magnetic chitosan/graphene oxide composite hydrogel beads for pH-dependent adsorption of anionic and cationic dyes: Mechanisms, optimization, and reusability.

International journal of biological macromolecules·2026
Same author

Explainable prediction of healthcare waste generation using hybrid PCA-GPR with SHAP for enhanced environmental protection.

Journal of environmental health science & engineering·2026
Same author

A standardized parametric weighting system for water quality indexing: advancing water resource management through improved decision support.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2026
Same author

A synthesis of human health and ecological risk assessment indicators for microplastics in Nigerian water systems.

International journal of environmental health research·2026

Related Experiment Video

Updated: Jul 4, 2025

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies
10:44

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies

Published on: July 1, 2016

11.5K

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic

Zaher Mundher Yaseen1, Wan Hanna Melini Wan Mohtar2, Raad Z Homod3

  • 1Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.

Chemosphere
|January 31, 2024
PubMed
Summary

This study introduces advanced models for predicting heavy metals like arsenic and zinc in marine sediments. A hybrid model, RVM-FPA, significantly improved prediction accuracy for environmental management.

Keywords:
Artificial intelligenceHeavy metalsSensitivity analysisSoil contamination

More Related Videos

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae
10:20

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae

Published on: July 10, 2015

16.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jul 4, 2025

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies
10:44

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies

Published on: July 1, 2016

11.5K
Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae
10:20

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae

Published on: July 10, 2015

16.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Environmental Science
  • Computational Chemistry
  • Marine Geochemistry

Background:

  • Anthropogenic activities contribute to heavy metal (HM) contamination in marine sediments.
  • Accurate modeling of arsenic (As) and zinc (Zn) is crucial for environmental risk assessment.
  • Standalone predictive models often have limitations in capturing complex HM dynamics.

Purpose of the Study:

  • To develop and evaluate standalone and hybrid models for predicting As and Zn concentrations in marine sediments.
  • To assess the predictive performance of Elman neural network (ENN), Boosted Tree algorithm (BTA), and relevance vector machine (RVM).
  • To enhance prediction accuracy using a hybrid RVM with a flower pollination algorithm (RVM-FPA).

Main Methods:

  • Implementation of standalone ENN, BTA, and RVM models for As and Zn prediction.
  • Development of a hybrid RVM-FPA model integrating heuristic optimization.
  • Evaluation using performance indicators (e.g., PBAIS, MAE), graphical methods, and cumulative probability functions (CDF).
  • Stationarity and reliability checks using Akaike (AIC) and Schwarz (SCI) information criteria with Dickey-Fuller (ADF) and Philip Perron (PP) tests.

Main Results:

  • RVM-M2 and ENN-M2 models showed the best performance for As and Zn prediction, respectively.
  • The hybrid RVM-FPA model demonstrated superior reliability and predictive accuracy compared to standalone models.
  • RVM-FPA achieved a 5% increase in predictive accuracy for As and an 18% increase for Zn.

Conclusions:

  • Intelligent data-driven models and heuristic optimization are effective for estimating complex HM concentrations.
  • The RVM-FPA model offers a reliable approach for predicting heavy metals in marine environments.
  • Findings provide valuable insights for environmental managers and stakeholders in developing effective strategies.