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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

337
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 of...
337
State Space Representation01:27

State Space Representation

476
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
476
Stability of structures01:14

Stability of structures

399
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
399
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

209
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...
209

You might also read

Related Articles

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

Sort by
Same author

FILM-Screen: A fully integrated microfluidic platform for point-of-care lung cancer screening via multiplexed mRNA profiling in blood.

Biosensors & bioelectronics·2026
Same author

Small Object Localization with 90% Annotation Reduction by Positive-Unlabeled Learning.

Micromachines·2025
Same author

Bio-inspired hierarchical GO/WS<sub>2</sub>/PANI heterostructure for high-performance room-temperature NH<sub>3</sub> sensing: Enabling real-time wireless health monitoring.

Talanta·2025
Same author

Crude toxin production and chemical control of Boeremia exigua.

Scientific reports·2025
Same author

ID-CRISPR: A CRISPR/Cas12a platform for label-free and sensitive detection of rare mutant alleles using self-interference DNA hydrogel reporter.

Biosensors & bioelectronics·2025
Same author

Appropriate amount of modified corn flour improves the structural and physicochemical properties of wheat dough.

Food chemistry: X·2025
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

Chaos (Woodbury, N.Y.)·2026
Same journal

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

Chaos (Woodbury, N.Y.)·2026
Same journal

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

Chaos (Woodbury, N.Y.)·2026
Same journal

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Dec 29, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Detecting network structures from measurable data produced by dynamics with hidden variables.

Rundong Shi1, Weinuo Jiang1, Shihong Wang1

  • 1School of Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Chaos (Woodbury, N.Y.)
|February 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces three network reconstruction methods to address issues caused by hidden variables. These novel approaches improve network analysis accuracy when data is incomplete.

More Related Videos

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

3.2K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

10.4K

Related Experiment Videos

Last Updated: Dec 29, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

3.2K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

10.4K

Area of Science:

  • Network Science
  • Data Analysis

Background:

  • Network structure reconstruction is crucial but challenged by unobserved variables.
  • Hidden variables in networks cause significant reconstruction errors, potentially rendering methods ineffective.

Purpose of the Study:

  • To develop and analyze methods for network reconstruction in the presence of hidden variables.
  • To address the limitations of existing methods when dealing with unavailable or unknown network variables.

Main Methods:

  • Proposing three distinct reconstruction methods: statistical characteristics, linearizable hidden variables, and white noise injection.
  • Providing theoretical analyses for each proposed method.
  • Conducting numerical simulations to validate theoretical derivations and assess robustness.

Main Results:

  • Demonstrated the effectiveness of the proposed methods in handling hidden variables.
  • Validated theoretical frameworks through comprehensive numerical results.
  • Showcased the robustness of the developed network reconstruction techniques.

Conclusions:

  • The developed methods offer viable solutions for network reconstruction problems with hidden variables.
  • The findings provide a strong foundation for practical experimental applications in network science.
  • This research enhances the reliability of network analysis in real-world scenarios with incomplete data.