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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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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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End Point Prediction: Gran Plot01:07

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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.
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Updated: Jun 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Multigranularity Information Fused Contrastive Learning With Multiview Clustering.

Hengrong Ju, Yang Lu, Weiping Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |June 11, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel multigranularity (MG) information fused contrastive learning for multiview clustering (MVC). MGCMVC enhances clustering by integrating low- and high-level features and preserving structural information, achieving state-of-the-art results.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Contrastive multiview clustering (MVC) leverages representation learning but traditionally ignores low-level features.
    • Existing methods struggle with quantifying view importance and incorporating structural information, limiting clustering performance.

    Purpose of the Study:

    • To propose a novel multigranularity (MG) information fused contrastive learning framework for MVC (MGCMVC).
    • To enhance clustering by integrating both low- and high-level features and preserving structural information.

    Main Methods:

    • Reconstructing low- and high-level features into fine- and coarse-granularity features.
    • Implementing an MG adaptive weighting sample-level contrastive learning mechanism for feature fusion and view quality adaptation.
    • Designing a structure-oriented cluster-level contrastive learning approach to maintain structural information and cross-view consistency.

    Main Results:

    • MGCMVC effectively fuses multigranularity features, improving clustering performance.
    • The method mitigates performance degradation due to variations in view quality.
    • Experiments on ten datasets confirm MGCMVC achieves state-of-the-art performance.

    Conclusions:

    • MGCMVC offers a superior approach to contrastive MVC by utilizing multigranularity features and structural information.
    • The proposed framework addresses key limitations in traditional contrastive MVC methods.
    • MGCMVC demonstrates significant improvements in clustering accuracy and robustness.