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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.3K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.3K
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

731
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
731
Bulk Density of Aggregate01:22

Bulk Density of Aggregate

1.3K
Bulk density refers to the mass of aggregate particles that would fill a unit volume. The concept of bulk density originates from the inability to pack aggregate particles in a manner that completely eliminates void spaces. Hence, the term bulk refers to the volume that encompasses both the aggregates and the voids. This measurement is crucial when aggregates are batched by volume and is used to convert quantities by mass to volume.
Most natural mineral aggregates, like sand and gravel,...
1.3K
Classification of Leukocytes01:30

Classification of Leukocytes

6.4K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Robust Personalized Federated Continual Learning via Explainable Multi-Granularity Prompt.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Routine biomarkers map response probability in HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2 blockade.

BMC cancer·2026
Same author

Correlation of vitamin D and diabetic peripheral neuropathic pain: A cross-sectional study.

The Journal of international medical research·2026
Same author

The Impact of Anthropomorphic Eco-Friendly Logos on Consumers' Green Purchase Intention: A Moderated Mediation Model.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Flipped classroom integrated with team-based learning enhances surgical skills in ophthalmology residency training: a randomized controlled trial.

Frontiers in medicine·2026
Same author

Gate-tunable bipolar transport in PbSe/GeSe/MoS<sub>2</sub> heterojunction photodetector array for medication optical rotation detection.

Optics express·2026

Related Experiment Video

Updated: Feb 22, 2026

Analysis and Specification of Starch Granule Size Distributions
08:46

Analysis and Specification of Starch Granule Size Distributions

Published on: March 4, 2021

5.7K

Local-Density-Based Optimal Granulation and Manifold Information Granule Description.

Ji Xu, Guoyin Wang, Tianrui Li

    IEEE Transactions on Cybernetics
    |September 26, 2017
    PubMed
    Summary

    This study introduces a local-density-based optimal granulation model (LoDOG) for efficient and accurate information granule construction. LoDOG detects arbitrary shapes and achieves optimal granulation with O(N) complexity, enhancing data reconstruction and interpretability.

    More Related Videos

    Fragmenting Bulk Hydrogels and Processing into Granular Hydrogels for Biomedical Applications
    10:18

    Fragmenting Bulk Hydrogels and Processing into Granular Hydrogels for Biomedical Applications

    Published on: May 17, 2022

    6.8K
    Gelatin Methacryloyl Granular Hydrogel Scaffolds: High-throughput Microgel Fabrication, Lyophilization, Chemical Assembly, and 3D Bioprinting
    10:36

    Gelatin Methacryloyl Granular Hydrogel Scaffolds: High-throughput Microgel Fabrication, Lyophilization, Chemical Assembly, and 3D Bioprinting

    Published on: December 9, 2022

    8.1K

    Related Experiment Videos

    Last Updated: Feb 22, 2026

    Analysis and Specification of Starch Granule Size Distributions
    08:46

    Analysis and Specification of Starch Granule Size Distributions

    Published on: March 4, 2021

    5.7K
    Fragmenting Bulk Hydrogels and Processing into Granular Hydrogels for Biomedical Applications
    10:18

    Fragmenting Bulk Hydrogels and Processing into Granular Hydrogels for Biomedical Applications

    Published on: May 17, 2022

    6.8K
    Gelatin Methacryloyl Granular Hydrogel Scaffolds: High-throughput Microgel Fabrication, Lyophilization, Chemical Assembly, and 3D Bioprinting
    10:36

    Gelatin Methacryloyl Granular Hydrogel Scaffolds: High-throughput Microgel Fabrication, Lyophilization, Chemical Assembly, and 3D Bioprinting

    Published on: December 9, 2022

    8.1K

    Area of Science:

    • Granular Computing
    • Data Mining
    • Machine Learning

    Background:

    • Information Granules (IGs) are crucial in granular computing.
    • The principle of justifiable granularity guides IG design.
    • Improving IG efficiency and accuracy remains an open challenge.

    Purpose of the Study:

    • To present a novel local-density-based optimal granulation model (LoDOG).
    • To address the limitations of existing methods in IG construction.
    • To enhance the efficiency and accuracy of information granulation.

    Main Methods:

    • Developed a local-density-based optimal granulation model (LoDOG).
    • Utilized landmark points on manifold skeletons to describe arbitrary IG shapes.
    • Introduced a dissimilarity metric for reconstruction quality evaluation.

    Main Results:

    • LoDOG detects arbitrarily-shaped information granules.
    • Achieves optimal granulation solutions with O(N) complexity post-tree construction.
    • Demonstrates approximate reconstruction capabilities of datasets.
    • Provides insights into the interpretability of LoDOG IGs.

    Conclusions:

    • LoDOG offers significant advantages in IG construction.
    • The model effectively handles arbitrarily-shaped IGs.
    • Theoretical and empirical results validate LoDOG's effectiveness and manifold description capabilities.