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 Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

Cyclic Processes And Isolated Systems

Cyclic Processes And Isolated Systems

A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state.
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...

You might also read

Related Articles

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

Sort by

Same author

The Enactive Torch: Open-source sensory substitution device for neurophysiological research.

HardwareX·2026

Same author

Perceptual sensitivity, but not metacognitive monitoring, is shaped by increases and decreases in control.

Experimental brain research·2026

Same author

Susceptibility for extremely low external fluctuations and critical behavior of Greenberg-Hastings neuronal model.

Physical review. E·2026

Same author

Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton.

Physical review. E·2026

Same author

Gaze crossing: a new paradigm to assess social contingency and word learning during real-time infant-adult interactions.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2026

Same author

Quantifying What Is Efficacious Yet Not Observable: Cognitive Neuroscience's Measurement Problem Has a Solution.

Cognitive science·2026

Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026

Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026

Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026

Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026

Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026

Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Beehive scale-free emergent dynamics.

Ivan Shpurov¹, Tom Froese², Dante R Chialvo^3,4

¹Embodied Cognitive Science Unit, Okinawa Institute of Science and Technology Graduate University, Tancha, Okinawa, Japan. ivan.shpurov@oist.jp.

Scientific Reports

|June 11, 2024

Summary

This summary is machine-generated.

Honeybee hive dynamics reveal complex collective behavior. Bees self-organize near optimal density, optimizing hive throughput through long-range correlations.

More Related Videos

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

Related Experiment Videos

Last Updated: Jun 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

Area of Science:

Complex systems
Collective dynamics
Social insect behavior

Background:

Social insects, like honeybees, display emergent properties in their collective dynamics.
Understanding these dynamics is key to comprehending complex system behavior.

Purpose of the Study:

To analyze honeybee hive collective dynamics using a previously published dataset.
To investigate spatial and temporal correlations in honeybee occupancy density.

Main Methods:

Individual tracking of thousands of honeybees over multiple days.
Analysis of occupancy density fluctuations and their relationship with bee flow.

Main Results:

Observed long-range spatial and temporal correlations in hive occupancy density.
Identified a non-monotonic function between honeybee density and flow, similar to traffic jamming.
Hive dynamics appear self-adjusted near a point of optimal density and maximum throughput.

Conclusions:

Honeybee hives exhibit self-organization towards optimal operating density.
Collective dynamics in beehives share similarities with other complex systems, like traffic flow.
This self-adjustment optimizes the overall efficiency and throughput of the honeybee colony.