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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

492
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
492
Flow Cytometry01:23

Flow Cytometry

15.7K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
15.7K
Rapidly Varying Flow01:24

Rapidly Varying Flow

451
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
451
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

434
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
434
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

466
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
466
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

528
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
528

You might also read

Related Articles

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

Sort by
Same author

<i>VFL-Cafe</i>: Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection.

Entropy (Basel, Switzerland)·2025
Same author

Game Theoretic Clustering for Finding Strong Communities.

Entropy (Basel, Switzerland)·2024
Same author

FedTKD: A Trustworthy Heterogeneous Federated Learning Based on Adaptive Knowledge Distillation.

Entropy (Basel, Switzerland)·2024
Same author

Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review.

Entropy (Basel, Switzerland)·2023
Same author

Structural Entropy of the Stochastic Block Models.

Entropy (Basel, Switzerland)·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: Jan 16, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.8K

Infodemic Source Detection with Information Flow: Foundations and Scalable Computation.

Zimeng Wang1, Chao Zhao1, Qiaoqiao Zhou2

  • 1Department of Computer Science, City University of Hong Kong, Hong Kong, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel generalized estimator for rumor source detection, improving upon traditional methods that fail in complex networks. The new approach enhances accuracy and scalability in identifying information origins during network spread.

Keywords:
infodemic source detectioninformation flowsubmodular optimization

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K

Related Experiment Videos

Last Updated: Jan 16, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.8K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K

Area of Science:

  • Network Science
  • Information Theory
  • Computational Social Science

Background:

  • Traditional rumor source identification methods, like maximum likelihood (ML) and joint maximum likelihood (JML) estimators using the Susceptible-Infectious (SI) model, suffer from degeneracy.
  • These classical approaches often fail to uniquely identify the rumor source, even in basic network configurations.

Purpose of the Study:

  • To develop a more robust and accurate method for identifying the source of rumors in networks.
  • To overcome the limitations of existing estimators by incorporating random observation times and advanced network flow concepts.

Main Methods:

  • Proposed a generalized estimator incorporating independent random observation times.
  • Formulated information flow beyond simple graphs, considering rate constraints and multicast capacities for cyclic polylinking networks.
  • Developed forward elimination and backward search algorithms for rate-constrained source detection.

Main Results:

  • The generalized estimator demonstrates improved performance in identifying rumor sources compared to classical methods.
  • Simulations validate the effectiveness and scalability of the proposed algorithms for rate-constrained source detection.
  • The study provides a rigorous foundation for infodemic source detection in complex network environments.

Conclusions:

  • The novel generalized estimator offers a significant advancement in rumor source detection, particularly in challenging network structures.
  • The developed algorithms are effective and scalable, providing practical tools for analyzing information spread.
  • This research establishes a robust framework for understanding and mitigating the impact of infodemics.