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Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Types of Aggregate Grading01:15

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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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Ethical Standards I01:25

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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
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Ethical Standards II01:23

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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
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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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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Related Experiment Video

Updated: Jan 11, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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Fine-Grained Personalized Data Aggregation Scheme with High Quality and Privacy Protection.

Zhuoyue Xia1, Raja Kumar Murugesan1

  • 1School of Computer Science, Taylor's University, Subang Jaya 47500, Malaysia.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving truth discovery framework for mobile crowd sensing (MCS). It enables accurate data aggregation while protecting user location and data privacy through personalized, task-specific weighting and encryption.

Keywords:
Paillier homomorphic encryptiondata aggregationfine-grainedmobile crowd sensingprivacy protectiontruth discovery

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Data Privacy

Background:

  • Mobile crowd sensing (MCS) requires truth discovery for aggregating noisy data.
  • Existing methods often compromise user privacy or lack task-specific reliability.

Purpose of the Study:

  • To develop a privacy-preserving truth discovery framework for MCS.
  • To enable high-quality data aggregation while protecting user location and data privacy.
  • To support fine-grained, task-level incentives.

Main Methods:

  • A task-wise, personalized, privacy-preserving truth discovery framework.
  • Per-user, per-task weight learning for aggregation.
  • Paillier homomorphic encryption for aggregate-only processing.
  • Task-scoped unlinkable pseudonyms for structural privacy.

Main Results:

  • Achieved high accuracy (MAE/RMSE ~10-5) compared to non-private baselines.
  • Demonstrated fast and stable convergence.
  • Showed predictable scaling with users, tasks, and key sizes.
  • Identified cloud-side decryption as the main computational cost.

Conclusions:

  • Personalized weighting and structural privacy offer practical, high-quality aggregation for privacy-critical MCS.
  • The framework effectively balances data utility with robust privacy protection.
  • Enables reliable data aggregation in MCS without compromising individual privacy.