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Related Concept Videos

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

156
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...
156
Aggregates Classification01:29

Aggregates Classification

340
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...
340
Types of Aggregate Grading01:15

Types of Aggregate Grading

569
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.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
569
Deleterious Substances in Aggregate01:25

Deleterious Substances in Aggregate

187
Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
Another type of impurity is clay and fine material that...
187
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

111
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
111
Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

212
The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
212

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Automating Aggregate Quantification in Caenorhabditis elegans
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Simultaneous selection and incorporation of consistent external aggregate information.

Yunxiang Huang1, Chiung-Yu Huang1, Mi-Ok Kim1

  • 1Department of Epidemiology & Biostatistics, University of California at San Francisco, San Francisco, California, USA.

Statistics in Medicine
|October 3, 2023
PubMed
Summary

This study introduces a penalized likelihood method to combine participant data with external aggregate information, even from different populations. The approach selects consistent external data, preventing bias and accounting for sampling errors.

Keywords:
empirical likelihoodinformation synthesismeta-analysispopulation heterogeneityregularization

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Data Science

Background:

  • Synthesizing participant data with external aggregate information is increasingly important.
  • Existing methods often assume homogeneity between internal studies and external sources, which is difficult to verify.
  • Violating this homogeneity assumption can lead to biased results.

Purpose of the Study:

  • To develop a penalized likelihood approach for synthesizing participant-level data with external aggregate information.
  • To address bias arising from heterogeneity in study populations.
  • To provide a flexible framework for incorporating consistent external information from diverse sources.

Main Methods:

  • A penalized likelihood approach is proposed for simultaneous selection and synthesis of external aggregate data.
  • The framework uses a semi-parametric density ratio model to account for differences in independent variable distributions.
  • A two-step estimator and optimization algorithm are developed for computation, addressing sampling errors in external data.

Main Results:

  • The proposed approach effectively avoids bias caused by population heterogeneity.
  • It successfully incorporates consistent external information from heterogeneous populations.
  • Selection and estimation consistency, along with asymptotic normality of the two-step estimator, are established.

Conclusions:

  • The penalized likelihood method offers a robust framework for data synthesis, overcoming limitations of homogeneity assumptions.
  • This approach enhances the reliability of combining diverse data sources in research.
  • The method is illustrated effectively using gestational weight gain management studies.