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 Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

119
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...
119
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

130
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...
130

You might also read

Related Articles

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

Sort by
Same author

GatorSC: multi-scale cell and gene graphs with mixture-of-experts fusion for single-cell transcriptomics.

Briefings in bioinformatics·2026
Same author

Development of novel reinforcement learning-based optimizer to impede tumor growth via radiochemotherapy.

Scientific reports·2026
Same author

Inflammatory Myofibroblastic Tumor of the Gallbladder Mimicking Malignancy: A Case Report and Literature Review.

Clinical case reports·2026
Same author

Defect Diamond-Like d<sup>10</sup> Metal Indium Selenium With Strong Second-Harmonic Generation and Enhanced Laser-Induced Damage Threshold.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Population-scale characterization of the oral microbiome and associations with metabolic health.

Nature communications·2026
Same author

Morphology-Aware Deep Features and Frozen Filters for Surgical Instrument Segmentation with LLM-Based Scene Summarization.

Journal of clinical medicine·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Aug 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

612

Prompt Deep Light-Weight Vessel Segmentation Network (PLVS-Net).

Muhammad Arsalan, Tariq M Khan, Syed Saud Naqvi

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 4, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Accurate retinal vessel segmentation is vital for diagnosing eye diseases. A new Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) improves accuracy and efficiency by incorporating structural context, outperforming existing methods.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    484

    Related Experiment Videos

    Last Updated: Aug 26, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    612
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    484

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Accurate retinal vessel segmentation is crucial for diagnosing and monitoring vision-threatening diseases like diabetic retinopathy and age-related macular degeneration.
    • Current encoder-decoder segmentation methods struggle to capture the contextual information of retinal vessel structures, leading to a semantic gap.

    Purpose of the Study:

    • To introduce a novel Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) for enhanced retinal vessel segmentation.
    • To address the limitations of existing methods in capturing contextual information and bridging the semantic gap.

    Main Methods:

    • The proposed PLVS-Net utilizes prompt blocks, each combining asymmetric kernel convolutions, depth-wise separable convolutions, and ordinary convolutions.
    • This approach aims to extract relevant features effectively while reducing the number of trainable parameters.

    Main Results:

    • The PLVS-Net demonstrated superior performance compared to existing methods.
    • The network achieved high accuracy in retinal vessel segmentation across three benchmark datasets: DRIVE, STARE, and CHASE.

    Conclusions:

    • The PLVS-Net offers an effective and efficient solution for retinal vessel segmentation.
    • The novel architecture successfully integrates contextual information, improving diagnostic capabilities for eye diseases.