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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.4K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

136
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...
136
Dot Product: Problem Solving01:21

Dot Product: Problem Solving

449
The dot product is a powerful tool in problem-solving involving vectors, given that the dot product of two vectors is the product of their magnitudes and the cosine of the angle between them measured anti-clockwise. Solving problems involving the dot product requires understanding its properties and developing a step-by-step process to solve them. Here are the main steps to follow when solving any general problem involving the dot product:
Identify the problem: Start by reading the problem and...
449
Parallel Processing01:20

Parallel Processing

265
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
265
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

311
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
311
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

196
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
196

You might also read

Related Articles

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

Sort by
Same author

CatDive: A simple yet effective method for maximizing category diversity in sequential recommendation.

PloS one·2026
Same author

Offline and online coupled tensor factorization with knowledge graph.

PloS one·2025
Same author

Accurate semi-supervised automatic speech recognition for ordinary and characterized speeches via multi-hypotheses-based curriculum learning.

PloS one·2025
Same author

Threshold-based exploitation of noisy label in black-box unsupervised domain adaptation.

PloS one·2025
Same author

Accurate multi-behavior sequence-aware recommendation via graph convolution networks.

PloS one·2025
Same author

Dependency-aware action planning for smart home.

PloS one·2024
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Sep 27, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

536

DAO-CP: Data-Adaptive Online CP decomposition for tensor stream.

Sangjun Son1, Yong-Chan Park1, Minyong Cho1

  • 1Seoul National University, Seoul, Republic of Korea.

Plos One
|April 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces DAO-CP, an accurate and efficient method for tensor stream decomposition. DAO-CP adapts to data changes by detecting error norm shifts and optimizing speed-accuracy trade-offs for improved performance.

Related Experiment Videos

Last Updated: Sep 27, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

536

Area of Science:

  • Data Science
  • Machine Learning
  • Applied Mathematics

Background:

  • Tensor decomposition is vital for feature extraction and data completion.
  • Real-world data streams are dynamic, posing challenges for static decomposition methods.
  • Existing dynamic tensor decomposition techniques often compromise accuracy for efficiency.

Purpose of the Study:

  • To develop an accurate and efficient online tensor decomposition method for dynamic data streams.
  • To address the limitations of current methods in handling inconsistent temporal patterns.
  • To improve the adaptability and performance of tensor decomposition in changing data environments.

Main Methods:

  • Proposed DAO-CP, an online CP decomposition algorithm.
  • Implemented change point detection for local error norms in tensor streams.
  • Developed a strategy to dynamically balance speed and accuracy by reusing or restarting decomposition.

Main Results:

  • DAO-CP demonstrates state-of-the-art accuracy in tensor stream decomposition.
  • The method achieves this accuracy without significant loss in computational speed.
  • DAO-CP effectively adapts to sudden changes in data patterns.

Conclusions:

  • DAO-CP offers a robust solution for accurate and efficient tensor stream decomposition.
  • The adaptive strategy significantly improves performance on dynamic and inconsistently changing data.
  • This method enhances the practical applicability of tensor decomposition in real-world scenarios.