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

130
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...
130
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

203
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...
203
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

177
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
177
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.9K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.9K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.9K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.9K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

709
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
709

You might also read

Related Articles

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

Sort by
Same author

Recent Advances in Molecular Engineering of Organelle-Targeted Viscosity Probes for Fluorescence and Biological Applications.

ACS sensors·2026
Same author

Constructing an evaluation system for the whole process of urban domestic waste disposal technology in high-altitude areas based on multiple analysis methods: Taking Lhasa as an example.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA·2026
Same author

Size-tunable and efficient fabrication of CsPbBr<sub>3</sub> superlattices by high-temperature self-assembly for superfluorescence.

Journal of colloid and interface science·2026
Same author

Integrative Analysis Reveals BPTF, COL1A1, and COL4A2 as Fibroblast-Related Biomarkers Associated with Immune Infiltration in Ovarian Cancer.

Current medicinal chemistry·2026
Same author

Deep learning models for predicting opaque bubble layer morphology of keratorefractive lenticule extraction before laser scanning.

Advances in ophthalmology practice and research·2026
Same author

Risk of Long-Term Clozapine Medication over Decades for Cardiac Adverse Events Including Heart Failure and Its Pathophysiology: A Japan and China Retrospective Cohort Analysis.

Medical sciences (Basel, Switzerland)·2026

Related Experiment Video

Updated: Oct 19, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K

Solving Periodic Investment Portfolio Selection Problems by a Data-Assisted Multiobjective Evolutionary Approach.

Jian Xiong, Rui Wang, Gang Kou

    IEEE Transactions on Cybernetics
    |September 20, 2021
    PubMed
    Summary

    This study introduces periodic investment portfolio selection problems (PIPSPs) for risk-averse investors. A novel data-assisted multiobjective evolutionary algorithm (DA-MOEA) effectively solves these complex financial planning challenges.

    More Related Videos

    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

    7.7K
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    5.2K

    Related Experiment Videos

    Last Updated: Oct 19, 2025

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.1K
    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

    7.7K
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    5.2K

    Area of Science:

    • Computational Finance
    • Optimization Algorithms
    • Financial Engineering

    Background:

    • Traditional portfolio selection often overlooks risk-averse investors' needs for long-term planning.
    • Investors increasingly seek strategies that maximize both final returns and portfolio flexibility.

    Purpose of the Study:

    • To introduce and model a new class of problems: periodic investment portfolio selection problems (PIPSPs).
    • To develop and validate a novel data-assisted multiobjective evolutionary algorithm (DA-MOEA) for solving PIPSPs.

    Main Methods:

    • A multiobjective model for PIPSPs was formulated.
    • A data-assisted MOEA (DA-MOEA) was proposed, integrating feature construction, data fusion, and information utilization.
    • Two DA-MOEA variants (DA-NSGA-II, DA-MOEA/D) were implemented and tested.

    Main Results:

    • Experimental results validated the efficacy of the proposed DA-MOEA approach for PIPSPs.
    • The data-assisted process demonstrated effectiveness in mining diverse data formats.
    • The impact of data source size, information type, and utilization strategy was analyzed.

    Conclusions:

    • The developed DA-MOEAs provide an effective solution for periodic investment portfolio selection problems.
    • The data-assisted methodology enhances the performance of evolutionary algorithms in complex financial optimization tasks.