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

Types of Coprecipitation01:10

Types of Coprecipitation

4.5K
Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
4.5K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

236
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
236
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

367
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
367
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

3.9K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
3.9K
Multiple Regression01:25

Multiple Regression

3.7K
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...
3.7K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

262
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
262

You might also read

Related Articles

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

Sort by
Same author

Pyrolytic hydrocarbon growth from cyclopentadiene.

The journal of physical chemistry. A·2010
Same author

In(III)-catalyzed tandem reaction of chromone-derived Morita-Baylis-Hillman alcohols with amines.

Organic & biomolecular chemistry·2010
Same author

Regression-based multi-trait QTL mapping using a structural equation model.

Statistical applications in genetics and molecular biology·2010
Same author

Elevated expression of APE1/Ref-1 and its regulation on IL-6 and IL-8 in bone marrow stromal cells of multiple myeloma.

Clinical lymphoma, myeloma & leukemia·2010
Same author

Accelerated aging of intervertebral discs in a mouse model of progeria.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society·2010
Same author

The synthesis of a multiblock osteotropic polyrotaxane by copper(I)-catalyzed huisgen 1,3-dipolar cycloaddition.

Macromolecular bioscience·2010

Related Experiment Video

Updated: Dec 22, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.3K

Vine copula selection using mutual information for hydrological dependence modeling.

Lingling Ni1, Dong Wang1, Jianfeng Wu1

  • 1Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, PR China.

Environmental Research
|May 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mutual information (MI)-based method for selecting vine copula structures in hydrological risk analysis. The approach effectively models complex dependencies, outperforming traditional methods.

Keywords:
Conditional mutual informationCopula entropyHydrological dependenceMutual informationVine structure selection

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.5K
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.5K

Related Experiment Videos

Last Updated: Dec 22, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.3K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.5K
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.5K

Area of Science:

  • Environmental Science
  • Hydrology
  • Statistics

Background:

  • Hydrological risk analysis requires understanding complex dependencies between multiple variables.
  • Vine copulas are increasingly used for multivariate modeling, but structure selection is crucial.
  • Existing methods for vine structure selection may not fully capture intricate dependence patterns.

Purpose of the Study:

  • To develop a model-independent, observation-based sequential approach for selecting vine copula structures using mutual information (MI).
  • To investigate the relationship between conditional mutual information (CMI) and copula entropy (CE) for vine structure selection.
  • To apply and evaluate the proposed MI-based approach for hydrological dependence modeling.

Main Methods:

  • Developed a sequential vine structure selection approach based on conditional mutual information (CMI) and copula entropy (CE).
  • Applied a statistical method-based truncation procedure to reduce the complexity of R-vine copulas.
  • Utilized two hydrological case studies: drought characterization (D-vine) and multi-site streamflow dependence (C-vine).

Main Results:

  • The MI-based approach successfully modeled diverse hydrological dependence structures, including D-vine and C-vine.
  • The proposed method provided more comprehensive information on variable dependencies compared to the traditional Kendall's tau-based approach.
  • The approach demonstrated effectiveness in capturing complex relationships in hydrological processes.

Conclusions:

  • The MI-based sequential approach offers a robust and effective method for vine copula structure selection in hydrological modeling.
  • This method enhances the understanding of multivariate dependencies in hydrological risk analysis and management.
  • The findings suggest a promising direction for improving dependence modeling in environmental and hydrological sciences.