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

112
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...
112
Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism

417
Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
417
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

95
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...
95
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

184
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
184
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

185
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
185
One-Way ANOVA01:18

One-Way ANOVA

8.6K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
8.6K

You might also read

Related Articles

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

Sort by
Same author

Multimodal radiomic analysis to determine high-Consistency prognostic phenotypes associated with epidermal growth factor receptor mutations in non-small cell lung cancer brain metastases.

European journal of radiology·2025
Same author

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

Computer methods and programs in biomedicine·2024
Same author

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan.

PloS one·2024
Same author

Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records.

Artificial intelligence in medicine·2023
Same author

Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics.

Entropy (Basel, Switzerland)·2023
Same author

A study of forecasting tennis matches via the Glicko model.

PloS one·2022
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: Oct 2, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Unraveling Hidden Major Factors by Breaking Heterogeneity into Homogeneous Parts within Many-System Problems.

Elizabeth P Chou1, Ting-Li Chen2, Hsieh Fushing3

  • 1Department of Statistics, National Chengchi University, Taipei 11605, Taiwan.

Entropy (Basel, Switzerland)
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

Analyzing complex systems reveals how heterogeneity hides key factors. Breaking down systems into homogeneous parts uncovers these hidden major factors, demonstrating the "More is Different" phenomenon.

Keywords:
CEDAMagnus effectRosenberg Self-Esteem Scaleconditional entropyheterogeneitymutual information

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

19.0K

Related Experiment Videos

Last Updated: Oct 2, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

19.0K

Area of Science:

  • Complex Systems Science
  • Data Analysis
  • Statistical Modeling

Background:

  • Many-System Problems (MSP) investigate how system heterogeneity impacts dynamics.
  • Heterogeneity can constrain and obscure essential structural mechanisms within complex systems.
  • Existing methods often fail to capture the full picture of response-vs-covariate (Re-Co) dynamics in heterogeneous ensembles.

Purpose of the Study:

  • To develop a computational protocol for identifying major factors in heterogeneous complex systems.
  • To demonstrate how heterogeneity constrains and hides essential factors.
  • To reveal fuller collections of major factors by analyzing homogeneous subsystems.

Main Methods:

  • Developed a Categorical Exploratory Data Analysis (CEDA)-based protocol for major factor selection.
  • Utilized information theory, specifically conditional mutual information and entropy, for selection criteria (C1-confirmable, C2-irreplaceable).
  • Evaluated conditional entropies using contingency tables with reliability checks against finite sample effects.

Main Results:

  • Demonstrated that system heterogeneity inherently constrains and hides major factors.
  • Showed that decomposing heterogeneity into homogeneous parts leads to the discovery of more complete factor collections.
  • Successfully applied the protocol to an artificial MSP and real-world datasets from Major League Baseball pitching dynamics.

Conclusions:

  • The developed protocol effectively uncovers hidden major factors in heterogeneous systems.
  • Decomposition into homogeneous parts is crucial for a comprehensive understanding of complex system dynamics.
  • The methodology offers a novel approach for analyzing complex data, with applications extending to psychological scales like the Rosenberg Self-Esteem Scale.