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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
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...
34

You might also read

Related Articles

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

Sort by
Same author

3D-SLARM: Practical Lossless Volumetric Image Compression via a 3D-Scanning Lightweight Autoregressive Model.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.

Nature communications·2025
Same author

Learning Lossless Compression for High Bit-Depth Volumetric Medical Image.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Application and Research Progress of Laser-Induced Breakdown Spectroscopy in Agricultural Product Inspection.

ACS omega·2024
Same author

Unsupervised Deep Exemplar Colorization via Pyramid Dual Non-Local Attention.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same journal

Vibrational and Structural Properties of Aqueous H<sub>2</sub>SO<sub>4</sub> and Na<sub>2</sub>SO<sub>4</sub> Systems from Ambient to Supercritical Conditions: A Comparative Study between GGA(-D3) and r2SCAN Functionals.

The journal of physical chemistry. A·2026
Same journal

The Sigma Ring and Other Distinctive Features of Surface Potentials of Group 1 Systems.

The journal of physical chemistry. A·2026
Same journal

Modeling DOTA Decarboxylation in the Context of α-Radiolysis Using DFT Calculations.

The journal of physical chemistry. A·2026
Same journal

Mode-Selective Dual-Level Vibrational Perturbation Theory Assisted by Machine Learning for Rotational and Vibrational Spectra of Benzoic Acid and Aspirin.

The journal of physical chemistry. A·2026
Same journal

On the Nonparametric Diabatization of Coupled Electronic States.

The journal of physical chemistry. A·2026
Same journal

Stability of Some Ternary 13-Atom Icosahedral Clusters Assessed with Geometric, Electronic, and Thermodynamic Criteria.

The journal of physical chemistry. A·2026
See all related articles

Related Experiment Video

Updated: May 15, 2025

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy
03:49

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy

Published on: June 10, 2019

7.2K

Research on Quantitative Analysis Method for Aluminum Alloy Using Backpropagation Artificial Neural Network Algorithm

Li Wang1, Li Xu2, Li Li3

  • 1Mathematics and Physics College, Bengbu University, Bengbu 233030, China.

The Journal of Physical Chemistry. A
|April 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a quantitative model using laser-induced breakdown spectroscopy (LIBS) and a backpropagation artificial neural network (BP-ANN) for rapid elemental analysis in 5052 aluminum-magnesium alloys. The developed method accurately determines elemental concentrations, enhancing quality control for aluminum alloys.

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

593
Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization
07:14

Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization

Published on: October 6, 2019

8.2K

Related Experiment Videos

Last Updated: May 15, 2025

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy
03:49

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy

Published on: June 10, 2019

7.2K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

593
Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization
07:14

Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization

Published on: October 6, 2019

8.2K

Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Rapid elemental analysis is crucial for aluminum alloy classification and quality assurance.
  • Existing methods may lack the speed or accuracy required for real-time industrial applications.

Purpose of the Study:

  • To develop a quantitative model for analyzing elemental distributions in 5052 Al-Mg alloy.
  • To combine Laser-Induced Breakdown Spectroscopy (LIBS) with a Backpropagation Artificial Neural Network (BP-ANN) for enhanced analytical performance.

Main Methods:

  • Utilized LIBS to collect spectral data from five 5052 Al-Mg alloy samples (940 total spectra).
  • Developed a BP-ANN model using 700 training and 225 testing data sets.
  • Focused on spectral lines for Aluminum (Al) at 396.15 nm and Magnesium (Mg) at 279.54 nm.

Main Results:

  • The BP-ANN model achieved high accuracy in predicting Al and Mg concentrations.
  • Coefficients of determination were 0.9862 for Al and 0.9646 for Mg.
  • Low root-mean-square errors (0.6609 for Al, 0.75005 for Mg) and mean absolute errors (1.0986 for Al, 0.5504 for Mg) were observed.

Conclusions:

  • The combined LIBS and BP-ANN approach offers an accurate and stable method for quantitative elemental analysis of aluminum alloys.
  • This technology facilitates rapid online analysis, supporting industrial quality control and material classification.
  • The model demonstrates significant potential for real-time elemental composition determination in metallic materials.