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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

263
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
263
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

134
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.
In the absence...
134
Ranks01:02

Ranks

272
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
272
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

315
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
315
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K

You might also read

Related Articles

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

Sort by
Same author

WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions.

Scientific data·2025
Same author

Machine Learning Classifier-Based Metrics Can Evaluate the Efficiency of Separation Systems.

Entropy (Basel, Switzerland)·2024
Same author

Network science and explainable AI-based life cycle management of sustainability models.

PloS one·2024
Same author

Iterative experimental design and identifiability analysis of composite material failure models.

Heliyon·2024
Same author

Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter.

Sensors (Basel, Switzerland)·2024
Same author

Selection of optimal validation methods for quantitative structure-activity relationships and applicability domain.

SAR and QSAR in environmental research·2023
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Aug 2, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K

Matrix factorization-based multi-objective ranking-What makes a good university?

János Abonyi1, Ádám Ipkovich1, Gyula Dörgő1

  • 1Eötvös Loránd Research Network - University of Pannonia Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.

Plos One
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

Non-negative matrix factorization (NMF) and Sum of Ranking Differences (SRD) offer a novel approach to many-objective ranking problems. This method effectively ranks universities by analyzing 46 variables, identifying key factors for academic excellence.

More Related Videos

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

7.6K
Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering
04:12

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering

Published on: June 23, 2023

697

Related Experiment Videos

Last Updated: Aug 2, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K
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

7.6K
Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering
04:12

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering

Published on: June 23, 2023

697

Area of Science:

  • Optimization and Data Science
  • Higher Education Research

Background:

  • Many-objective ranking problems suffer from Pareto front oversaturation.
  • Traditional methods like Principal Component Analysis (PCA) can incorporate negatively correlated objectives, hindering effective ranking.
  • Non-negative matrix factorization (NMF) offers a way to aggregate objectives for better Pareto front analysis.

Purpose of the Study:

  • To develop and validate a novel method for many-objective ranking using Non-negative Matrix Factorization (NMF) and Sum of Ranking Differences (SRD).
  • To establish an unbiased ranking of 1176 universities based on 46 variables from the CWTS Leiden Ranking 2020.
  • To compare the performance of NMF against PCA and sparse NMF in ranking capabilities.

Main Methods:

  • Applied Non-negative Matrix Factorization (NMF) to aggregate many objectives in ranking problems.
  • Utilized the Sum of Ranking Differences (SRD) method to identify and validate key objectives influencing the ranking.
  • Employed Relative Entropy (RE) and Hypervolume (HV) measures to evaluate ranking performance.
  • Ranked 1176 universities using 46 variables from the CWTS Leiden Ranking 2020.

Main Results:

  • NMF effectively handles non-negative correlations, enabling proper Pareto front application, unlike PCA.
  • The combined NMF-SRD method produced an unbiased ranking, identifying Harvard, Rockefeller, and Stanford as top universities.
  • Sparse NMF demonstrated the most informative ranking capabilities.
  • Decreasing unknown open-access publications and short-distance collaborations positively impacts academic excellence.

Conclusions:

  • The NMF-SRD approach provides a robust solution for many-objective university ranking.
  • Academic ranking is positively influenced by factors such as author count, long-distance collaborations, scientific impact, and citations.
  • Gender indicators show minimal correlation with scientific impact.