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

88
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...
88
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

128
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
128
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

461
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
461
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

692
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
692
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

100
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
100

You might also read

Related Articles

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

Sort by
Same author

Histone Lactylation Enhances Th17 Cell Differentiation Through DPP4 to Promote Epithelial-Mesenchymal Transition in Asthma.

Lung·2026
Same author

A molecular-imprinted SERS sensor based on a silver substrate for the selective capture and sensitive detection of crystal violet in textile wastewater.

Analytical methods : advancing methods and applications·2026
Same author

Nonmetal Single-Tellurium-Atom Electrocatalysts for Efficient Oxygen Evolution Reaction.

The journal of physical chemistry letters·2026
Same author

RUNX1 Directly Activates WNT2 to Orchestrate Tumor-Associated Macrophage Reprogramming in Colorectal Cancer.

Molecular carcinogenesis·2026
Same author

The impact of Pt particle size distribution on the HER pathway and performance.

Chemical communications (Cambridge, England)·2026
Same author

A dual-functional flexible oxidized carbon cloth (OCC)/MoSâ‚‚ substrate for in-situ SERS detection and on-site photocatalytic degradation of organic contaminants in aquatic ecosystems.

Mikrochimica acta·2026

Related Experiment Video

Updated: Aug 3, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K

A Novel Swarm Exploring Varying Parameter Recurrent Neural Network for Solving Non-Convex Nonlinear Programming.

Zhijun Zhang, Xiaohui Ren, Jilong Xie

    IEEE Transactions on Neural Networks and Learning Systems
    |April 11, 2023
    PubMed
    Summary

    A novel swarm exploring varying parameter recurrent neural network (SE-VPRNN) method efficiently solves non-convex nonlinear programming. This approach combines recurrent neural networks with particle swarm optimization, demonstrating superior accuracy and faster convergence than existing algorithms.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.4K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K

    Related Experiment Videos

    Last Updated: Aug 3, 2025

    SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
    08:13

    SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

    Published on: December 25, 2017

    8.2K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.4K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K

    Area of Science:

    • Computational Mathematics
    • Artificial Intelligence
    • Optimization Algorithms

    Background:

    • Non-convex nonlinear programming presents significant computational challenges.
    • Existing methods often struggle with efficiency and accuracy in finding optimal solutions.

    Purpose of the Study:

    • To propose a novel method, the swarm exploring varying parameter recurrent neural network (SE-VPRNN), for efficient and accurate non-convex nonlinear programming.
    • To enhance global searching capabilities through wavelet mutation.

    Main Methods:

    • Utilizing a varying parameter recurrent neural network to accurately search for local optimal solutions.
    • Integrating a particle swarm optimization (PSO) framework for information exchange and updating particle velocities and positions.
    • Applying wavelet mutation to enhance particle diversity and global searching ability.

    Main Results:

    • The SE-VPRNN method effectively solves non-convex nonlinear programming problems.
    • Computer simulations confirm the method's effectiveness.
    • The proposed method shows advantages in accuracy and convergence time compared to three existing algorithms.

    Conclusions:

    • The SE-VPRNN method offers an effective solution for non-convex nonlinear programming.
    • The integration of recurrent neural networks and PSO with wavelet mutation improves performance.
    • This approach provides a promising alternative for complex optimization problems.