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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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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.
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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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...
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Segregation in Fresh Concrete01:16

Segregation in Fresh Concrete

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Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
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Related Experiment Video

Updated: Mar 24, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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GrCS: Granular Computing-Based Crowd Segmentation.

Ven Jyn Kok, Chee Seng Chan

    IEEE Transactions on Cybernetics
    |March 19, 2016
    PubMed
    Summary

    This study introduces granular computing (GrCS) for crowd segmentation, a challenging task in computer vision. GrCS effectively outlines crowd boundaries by analyzing pixel correlations, improving crowd analysis.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Crowd segmentation is crucial for crowd analysis tasks like density estimation and behavior understanding.
    • Challenges include interocclusions, perspective distortion, and cluttered backgrounds, making localization difficult.

    Purpose of the Study:

    • To propose a novel crowd segmentation framework using granular computing (GrCS).
    • To conceptualize crowd segmentation at multiple granularity levels and map problems into tractable subproblems.

    Main Methods:

    • Exploiting correlations among pixel granules to aggregate structurally similar pixels into atomic structure granules.
    • Inferring crowd and background regions through granular information classification based on structure granules.

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    Main Results:

    • The GrCS framework effectively outlines natural boundaries between crowd and background regions.
    • Demonstrated scene-independence and effectiveness across diverse crowd scenes and crowdedness levels.

    Conclusions:

    • GrCS provides a robust approach to crowd segmentation by leveraging granular computing principles.
    • The method's ability to exploit granule correlations enhances the accuracy of crowd and background region delineation.