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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K
Reinforcement01:23

Reinforcement

1.1K
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
1.1K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.3K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.3K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.9K
Observational Learning01:12

Observational Learning

1.1K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.1K
Multimachine Stability01:25

Multimachine Stability

600
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
600

You might also read

Related Articles

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

Sort by
Same author

QRBT: Quantum Driven Reinforcement Learning for Scalable Blockchain Transaction Processing.

PloS one·2026
Same author

Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN.

Health information science and systems·2024
Same author

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds.

The European physical journal. Special topics·2022
Same author

COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds.

The European physical journal. Special topics·2022

Related Experiment Video

Updated: Mar 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

QRGEC: quantum reinforcement learning with golden jackal optimization for resilient edge cloud coordination in

Kranthi Kumar Lella1, Mallu Shiva Rama Krishna2

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. kranthikumar.l@manipal.edu.

Scientific Reports
|March 10, 2026
PubMed
Summary

Quantum Reinforcement Learning with Golden Jackal Optimization (QRGEC) enhances edge cloud coordination for Internet computing. This resilient framework improves energy efficiency and adaptability in dynamic environments.

Keywords:
Edge cloud coordinationGolden jackal optimizationQuantum cognitionQuantum reinforcement learningResilient internet computingSDG (sustainable development goals)

Related Experiment Videos

Last Updated: Mar 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Area of Science:

  • Computer Science
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Existing edge cloud coordination mechanisms struggle with resilience, energy efficiency, and adaptability in dynamic Internet computing environments.
  • Current optimization and learning methods exhibit slow convergence and limited robustness for distributed edge cloud resource management.

Purpose of the Study:

  • To introduce QRGEC (Quantum Reinforcement Learning with Golden Jackal Optimization) for resilient edge cloud coordination.
  • To enhance distributed Internet computing optimization through quantum-enhanced policy exploration and adaptive metaheuristic tuning.

Main Methods:

  • Utilizing variational quantum circuits for policy representation to explore high-dimensional decision spaces.
  • Employing Golden Jackal Optimization to adapt reinforcement learning parameters for improved convergence and learning speed.
  • Implementing a resilience-aware scheduler to balance energy efficiency, latency, and recovery in edge cloud workloads.

Main Results:

  • QRGEC achieved a 36.8% latency reduction, 24.7% increase in energy efficiency, and 48.2% improvement in resilience compared to baseline methods.
  • Demonstrated sustained resource utilization of 94% and autonomous recovery from network congestion and failures.
  • Successfully balanced latency-energy trade-offs and conserved energy in heterogeneous edge and cloud environments.

Conclusions:

  • QRGEC offers a robust and efficient solution for edge cloud coordination in Internet computing.
  • The framework demonstrates significant improvements in performance, resilience, and energy conservation.
  • QRGEC shows promise for autonomous management and optimization of dynamic distributed systems.