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

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.8K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.8K
Randomized Experiments01:13

Randomized Experiments

8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Associative Learning01:27

Associative Learning

1.2K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.2K
Reinforcement01:23

Reinforcement

826
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:
826
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
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...
2.5K
Observational Learning01:12

Observational Learning

824
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...
824

You might also read

Related Articles

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

Sort by
Same authorSame journal

Operational intelligence for institutional processes: dynamic modeling and state-based decision policies.

Frontiers in artificial intelligence·2026
Same author

Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.

Frontiers in artificial intelligence·2026
Same author

Public health risk stratification using hybrid machine learning: a reproducible analysis of performance, stability, and risk attribution.

Frontiers in bioinformatics·2026
Same author

Technical evaluation of language models adapted for the automation of legal contracts: clause extraction, classification, and summarization.

Frontiers in artificial intelligence·2026
Same author

Autonomous cyber-physical security middleware for IoT: anomaly detection and adaptive response in hybrid environments.

Frontiers in artificial intelligence·2026
Same author

Implementing federated learning for privacy-preserving emotion detection in educational environments.

Frontiers in artificial intelligence·2025

Related Experiment Video

Updated: May 5, 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

1.3K

Adaptive consensus optimization in blockchain using reinforcement learning and validation in adversarial

Rommel Gutierrez1, William Villegas-Ch1, Jaime Govea1

  • 1Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, Ecuador.

Frontiers in Artificial Intelligence
|October 16, 2025
PubMed
Summary

This study introduces an adaptive blockchain consensus architecture using reinforcement learning to enhance security and efficiency. The system improves throughput, reduces latency, and lowers energy consumption under adverse network conditions.

Keywords:
adaptive consensus mechanismartificial intelligenceenergy-efficient edge validationmalicious node detectionreinforcement learning in blockchain

Related Experiment Videos

Last Updated: May 5, 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

1.3K

Area of Science:

  • Blockchain Technology
  • Artificial Intelligence
  • Network Security

Background:

  • Modern blockchain networks face challenges with complexity and decentralization, limiting traditional consensus protocols under adverse conditions.
  • Existing systems struggle with real-time anomalies like Sybil attacks and network congestion, leading to performance degradation and security vulnerabilities.
  • Lack of autonomous policy adjustment mechanisms hinders adaptability, especially in resource-constrained edge computing environments.

Purpose of the Study:

  • To propose an adaptive consensus architecture for blockchain networks.
  • To enhance resilience against adversarial scenarios and dynamic network conditions.
  • To improve efficiency, security, and energy consumption in decentralized systems.

Main Methods:

  • Integration of a graph-based Proximal Policy Optimization (PPO) reinforcement learning agent.
  • Training the agent on a hybrid dataset of real traffic and synthetic adversarial behaviors.
  • Evaluation in stress-testing environments with multiple threat vectors, including Sybil attacks and network congestion.

Main Results:

  • Maintained stable throughput (TPS) and reduced consensus latency by 34% under high-load conditions.
  • Achieved high detection rates for Sybil and node-collapse scenarios (DR > 0.90, FPR < 0.10).
  • Demonstrated up to 16% lower energy consumption in high-congestion and crash-prone scenarios, with stable convergence and adaptation.

Conclusions:

  • The proposed adaptive consensus architecture effectively enhances blockchain performance and security.
  • Reinforcement learning integration provides robust adaptation to dynamic and adversarial network conditions.
  • The system shows significant potential for real-world deployment in edge computing and beyond.