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

Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

368
Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
368
Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

5.6K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
5.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
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...
47
Centrifugation01:05

Centrifugation

2.1K
Centrifugation is a separation technique based on differences in density or size. It is commonly used to separate solids from aqueous interferents. During centrifugation, the sample is placed in centrifugation tubes and spun at high angular velocity, which allows centrifugal force to act differentially on the different densities or masses of the components. After spinning, the supernatant liquid is decanted. Depending on the specific application, either the pellet or the supernatant is retained...
2.1K
Capillary Electrophoresis: Applications01:30

Capillary Electrophoresis: Applications

370
Capillary electrophoretic separations offer various modes, each with unique applications. These modes include capillary zone electrophoresis, capillary gel electrophoresis, capillary array electrophoresis, capillary isoelectric focusing, capillary isotachophoresis, micellar electrokinetic chromatography, and capillary electrochromatography.
Capillary zone electrophoresis (CZE) separates ionic components based on their electrophoretic mobility. It has been used to separate proteins, amino acids,...
370

You might also read

Related Articles

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

Sort by
Same author

WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions.

Scientific data·2025
Same author

Network science and explainable AI-based life cycle management of sustainability models.

PloS one·2024
Same author

Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter.

Sensors (Basel, Switzerland)·2024
Same author

Matrix factorization-based multi-objective ranking-What makes a good university?

PloS one·2023
Same author

3D Scanner-Based Identification of Welding Defects-Clustering the Results of Point Cloud Alignment.

Sensors (Basel, Switzerland)·2023
Same author

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0.

Sensors (Basel, Switzerland)·2023
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Machine Learning Classifier-Based Metrics Can Evaluate the Efficiency of Separation Systems.

Éva Kenyeres1, Alex Kummer1, János Abonyi1

  • 1HUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. Box 158, H-8200 Veszprém, Hungary.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning metrics like entropy and information gain can evaluate separation system efficiency. These novel metrics, validated on a waste-sorting model, offer a new way to assess operational performance.

Keywords:
Monte Carlo simulationclassifiersprocess developmentstochastic modelwaste sorting

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

8.9K

Related Experiment Videos

Last Updated: Jun 19, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

8.9K

Area of Science:

  • Environmental Engineering
  • Computer Science
  • Data Science

Background:

  • Traditional methods for evaluating separation systems often lack precision.
  • Machine learning metrics are widely used for classifier model evaluation.
  • There is a need for robust metrics to assess separation system efficiency.

Purpose of the Study:

  • To adapt machine learning metrics for evaluating separation systems.
  • To develop novel entropy- and information gain-based metrics for this purpose.
  • To validate these metrics using simulation experiments.

Main Methods:

  • Utilized entropy and information gain from machine learning.
  • Developed new metrics based on these concepts for separation systems.
  • Employed the receiver operating characteristic (ROC) curve to find optimal cut points.
  • Conducted simulation experiments on a stochastic waste-sorting system model.

Main Results:

  • Demonstrated the applicability of machine learning metrics to separation systems.
  • Validated the proposed entropy- and information gain-based metrics.
  • The receiver operating characteristic (ROC) curve effectively identified optimal operational points.
  • Simulation results confirmed the metrics' utility in evaluating system performance.

Conclusions:

  • Machine learning metrics provide a powerful framework for assessing separation system effectiveness.
  • The developed metrics offer a quantitative approach to optimizing separation processes.
  • This study bridges machine learning and separation science for improved system evaluation.