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

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

238
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...
238
Rapidly Varying Flow01:24

Rapidly Varying Flow

192
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...
192
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

186
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...
186

You might also read

Related Articles

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

Sort by
Same author

Hippocampal Glutamatergic Hyperactivation Mediates High-Loading Intensity of Exercise-Induced Cognitive Deficits Via HPC-mPFC Circuit Dysfunction.

CNS neuroscience & therapeutics·2026
Same author

Allosteric Inhibition of Polycomb Repressive Complex 2 by an EZH2-Selective Small Molecule Inhibitor.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Shear wave elastography-based predictive model for early functional recovery following extensor tendon repair of the hand.

BMC medical imaging·2026
Same author

Maximum likelihood estimation of perceptual differences in sorting tasks.

PloS one·2026
Same author

Laroprovstat, the First Oral Small-Molecule PCSK9 Inhibitor for the Treatment of Hypercholesterolemia: Results From a Randomized, Single-Blind, Placebo-Controlled Phase 1 Trial in Treatment-Naïve Patients.

Circulation·2026
Same author

Corrigendum to "Enhanced electric field intensity in transverse microchannel charcoal electrode for facilitating electrochemical demulsification rate of oil-in-water droplets" [J Hazard Mater 511 (2026) 142186].

Journal of hazardous materials·2026

Related Experiment Video

Updated: Nov 4, 2025

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.2K

Graph Regularized Flow Attention Network for Video Animal Counting From Drones.

Pengfei Zhu, Tao Peng, Dawei Du

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 28, 2021
    PubMed
    Summary
    This summary is machine-generated.

    We introduce AnimalDrone, a large-scale drone-collected dataset for animal counting in agriculture and wildlife protection. Our novel Graph Regularized Flow Attention Network (GFAN) accurately estimates animal density in diverse video conditions.

    More Related Videos

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
    08:32

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

    Published on: June 15, 2020

    12.9K
    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
    06:19

    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

    Published on: August 16, 2024

    636

    Related Experiment Videos

    Last Updated: Nov 4, 2025

    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.2K
    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
    08:32

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

    Published on: June 15, 2020

    12.9K
    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
    06:19

    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

    Published on: August 16, 2024

    636

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Ecological Monitoring

    Background:

    • Accurate animal counting is crucial for wildlife protection and agricultural management.
    • Existing datasets lack scale and diversity for drone-based animal counting.
    • Challenges include varying densities, altitudes, and perspectives in aerial footage.

    Purpose of the Study:

    • To introduce the AnimalDrone dataset, a large-scale, drone-collected video dataset for animal counting.
    • To develop an effective deep learning model for animal density estimation from drone imagery.
    • To provide a benchmark for evaluating animal counting algorithms in real-world scenarios.

    Main Methods:

    • Collected and annotated the AnimalDrone dataset with over 4 million objects across 53,644 frames.
    • Developed a Graph Regularized Flow Attention Network (GFAN) for density map estimation.
    • Utilized optical flow for temporal feature warping and introduced multi-granularity loss functions.

    Main Results:

    • The GFAN model demonstrated superior performance compared to state-of-the-art counting algorithms.
    • The multi-granularity loss function improved focus on discriminative features across scales.
    • Graph regularization ensured temporal coherence in density map predictions.

    Conclusions:

    • The AnimalDrone dataset provides a valuable resource for advancing animal counting research.
    • The proposed GFAN method offers an effective solution for density estimation in challenging drone-based video data.
    • The study highlights the potential of AI-powered drone surveillance for conservation and agriculture.