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

Aggregates Classification01:29

Aggregates Classification

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...
Types of Aggregate Grading01:15

Types of Aggregate Grading

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...
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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 result...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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 sampled...

You might also read

Related Articles

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

Sort by
Same author

Immunomodulatory effects of cannabis use: a multi-omics study in people living with HIV.

Brain, behavior, & immunity - health·2026
Same author

Multi-omics Data Integration.

Advances in experimental medicine and biology·2026
Same author

HIV immunological non-responders show low SKAP1 concentration and DNA hypermethylation in the SKAP1 promotor region: Low Skap1 in HIV Immunological Non-Responders.

AIDS (London, England)·2026
Same author

Interferon-related inflammaging links epigenetic age acceleration to multimorbidity.

Cell genomics·2026
Same author

Molecular signatures and causal factors underlying latent cytomegalovirus infection among people living with HIV (PLHIV).

Nature communications·2026
Same author

Analysis of microbiome high-dimensional experimental design data using generalized linear models and ANOVA simultaneous component analysis.

Frontiers in microbiomes·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

To aggregate or not to aggregate high-dimensional classifiers.

Cheng-Jian Xu1, Huub C J Hoefsloot, Age K Smilde

  • 1Biosystems Data Analysis group, University of Amsterdam, Amsterdam, The Netherlands.

BMC Bioinformatics
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

Aggregating Principal Component Discriminant Analysis (PCDA) models improves prediction performance and stability for high-dimensional data analysis in genomics and other fields. This approach offers a robust solution to statistical challenges posed by large datasets with fewer samples.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Machine Learning in Biology

Background:

  • High-throughput functional genomics generates vast datasets with numerous measurements per sample but limited sample sizes.
  • This data imbalance presents significant statistical challenges for analysis.
  • Novel analytical approaches are crucial for extracting meaningful insights from such data.

Purpose of the Study:

  • To evaluate an aggregated Principal Component Discriminant Analysis (PCDA) approach for analyzing high-dimensional biological data.
  • To assess the performance and stability of aggregated PCDA models compared to standard methods.
  • To address the statistical challenges inherent in analyzing high-throughput functional genomics data.

Main Methods:

  • Utilized Principal Component Discriminant Analysis (PCDA), an adaptation of Linear Discriminant Analysis (LDA) for high-dimensional data.
  • Employed repeated double cross-validation to generate multiple PCDA model versions.
  • Aggregated multiple PCDA models and used majority voting for final classification.

Main Results:

  • The aggregated PCDA approach demonstrated improved prediction performance.
  • The method provided more stable classification results.
  • The aggregation technique offered insights into model variability.

Conclusions:

  • Aggregating PCDA learners enhances prediction accuracy and result stability.
  • This method provides a valuable tool for understanding model variability in high-dimensional data.
  • Limitations and disadvantages of the aggregation approach were also considered.