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

What is Evolutionary History?02:35

What is Evolutionary History?

43.3K
Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
43.3K
Evolutionary Psychology01:20

Evolutionary Psychology

990
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
990
Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

362
In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...
362
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

6.8K
Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
6.8K
Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

3.7K
Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
3.7K
Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

4.0K
As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
4.0K

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K

Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach.

Chongwu Dong1, Wushao Wen2

  • 1School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China. dongchw@mail2.sysu.edu.cn.

Sensors (Basel, Switzerland)
|February 15, 2019
PubMed
Summary
This summary is machine-generated.

Mobile edge computing (MEC) offloads tasks but faces resource limits. A new decentralized strategy using evolutionary game theory balances user Quality of Service (QoS) and network costs effectively.

Keywords:
evolutionary game theorymobile edge computingtask offloading

More Related Videos

Performing Behavioral Tasks in Subjects with Intracranial Electrodes
12:10

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Published on: October 2, 2014

11.8K
Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
06:31

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

Published on: October 6, 2023

3.1K

Related Experiment Videos

Last Updated: Jan 29, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K
Performing Behavioral Tasks in Subjects with Intracranial Electrodes
12:10

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Published on: October 2, 2014

11.8K
Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
06:31

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

Published on: October 6, 2023

3.1K

Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Mobile edge computing (MEC) addresses mobile device resource limitations by offloading tasks to edge nodes.
  • Edge nodes have finite resources, potentially insufficient for high task volumes, necessitating coordinated cloud architectures.

Purpose of the Study:

  • To develop a dynamic, decentralized resource allocation strategy for task offloading in a multi-user, multi-edge, and central cloud environment.
  • To ensure user Quality of Service (QoS) while minimizing network service provider costs.

Main Methods:

  • Utilized evolutionary game theory to model resource competition among multi-users.
  • Employed replicator dynamics to analyze the decentralized resource allocation strategy.
  • Provided mathematical analysis to prove the strategy's stability and fairness.

Main Results:

  • Achieved an evolutionary equilibrium that satisfies user QoS requirements within edge node resource constraints.
  • Demonstrated the strategy's effectiveness in simulations, outperforming alternative methods.

Conclusions:

  • The proposed evolutionary game theory-based strategy offers an effective solution for dynamic resource allocation in MEC systems.
  • The strategy ensures QoS and stability while managing resource competition in heterogeneous cloud environments.