Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Entropy Changes Accompanying Specific Processes

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

You might also read

Related Articles

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

Sort by

Same author

Hippocampal neuronal hypoexcitability contributes to PTSD-like phenotypes in the experimental autoimmune encephalomyelitis model.

Frontiers in psychiatry·2026

Same author

Polydopamine-assisted polyethyleneimine functionalization of bacterial cellulose aerogel for efficient adsorptive removal of Cu(II) and organic dyes.

International journal of biological macromolecules·2026

Same author

Diffusion-Selective Tandem Catalysis for Alkane Hydroisomerization.

Angewandte Chemie (International ed. in English)·2026

Same author

Association between iron homestasis and all-cause mortality in acute pancreatitis: A retrospective MIMIC-IV database analysis.

Medicine·2025

Same author

Novel binding mode for negative allosteric NMDA receptor modulators.

The Journal of general physiology·2025

Same author

Correction: GluN2A-NMDA receptor inhibition disinhibits the prefrontal cortex, reduces forced swim immobility, and impairs sensorimotor gating.

Acta pharmacologica Sinica·2025

Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026

Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026

Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026

Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026

Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026

Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 10, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Mining human periodic behaviors via tensor factorization and entropy.

Feng Yi¹, Lei Su¹, Huaiwen He¹

¹School of Computer Science, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, Guangdong Province, China.

Peerj. Computer Science

|March 4, 2024

Summary

This summary is machine-generated.

This study introduces the Mobility Intention and Relative Entropy (MIRE) model to accurately detect human periodic behaviors using location data. The MIRE model effectively uncovers hidden mobility patterns, outperforming existing methods.

Keywords:

CP decomposition Human periodic behaviors Mobility intention Relative entropy Spatiotemporal data mining

More Related Videos

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Related Experiment Videos

Last Updated: May 10, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Area of Science:

Human mobility patterns
Data mining
Behavioral science

Background:

Human periodic behaviors are vital for applications but challenging to model accurately.
Existing methods struggle with location periodicity and precision in detecting behavioral oscillations.

Purpose of the Study:

To propose a novel model for accurately uncovering human periodic behaviors.
To leverage location periodicity and improve the accuracy of detecting behavioral oscillations.

Main Methods:

Employing tensor decomposition to extract mobility intentions from spatiotemporal data.
Utilizing subsequences with shared mobility intentions for periodicity mining.
Introducing a new periodicity detection algorithm based on relative entropy.

Main Results:

The proposed Mobility Intention and Relative Entropy (MIRE) model demonstrates effectiveness in uncovering human periodic behaviors.
Experimental results on real-world datasets confirm the model's accuracy.
Comparative analysis shows superior performance against baseline periodicity detection algorithms.

Conclusions:

The MIRE model offers a significant advancement in understanding and modeling human periodic behaviors.
The approach successfully addresses limitations in leveraging location periodicity and achieving high accuracy.