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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

206
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
206
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.3K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.3K
Dynamic Equilibrium02:20

Dynamic Equilibrium

57.5K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
57.5K
Decision Making01:20

Decision Making

358
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
358
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.9K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.9K
Observational Learning01:12

Observational Learning

405
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...
405

You might also read

Related Articles

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

Sort by
Same author

A systematic review on wearable-enabled remote health monitoring.

Digital health·2026
Same author

Literature Review of Deep-Learning-Based Detection of Violence in Video.

Sensors (Basel, Switzerland)·2024
Same author

Digital Twin Platform for Water Treatment Plants Using Microservices Architecture.

Sensors (Basel, Switzerland)·2024
Same author

Effect of Vagus Nerve Stimulation on the GASH/Sal Audiogenic-Seizure-Prone Hamster.

International journal of molecular sciences·2024
Same author

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Journal of integrative bioinformatics·2023
Same author

Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems.

Journal of medical Internet research·2023

Related Experiment Video

Updated: Oct 22, 2025

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

13.1K

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market.

María E Pérez-Pons1, Ricardo S Alonso1,2, Oscar García1

  • 1BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo 2, 37007 Salamanca, Spain.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary

A multi-agent system (MAS) aids sustainable agricultural purchasing by evaluating supplier sustainability and forecasting market prices. This system helps buyers make informed decisions amidst price volatility and environmental challenges.

Keywords:
IoTdecision support systemsdeep Q-learningedge computingmulti-agent systemssustainable agriculture

More Related Videos

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.2K
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

849

Related Experiment Videos

Last Updated: Oct 22, 2025

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

13.1K
Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.2K
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

849

Area of Science:

  • Agricultural Economics
  • Artificial Intelligence
  • Sustainable Agriculture

Background:

  • Increasing global population necessitates enhanced agricultural production for food security.
  • Climate change, resource depletion, and extreme weather pose significant threats to agricultural sustainability and productivity.
  • Price volatility and lack of transparency in agricultural markets complicate purchasing decisions.

Purpose of the Study:

  • To develop a multi-agent system (MAS) to support decision-making in purchasing sustainable agricultural products.
  • To provide tools for evaluating supplier sustainability and forecasting agricultural market prices.
  • To integrate user preferences with dynamic market information for optimized purchasing.

Main Methods:

  • Implementation of a MAS with supplier preference-based sustainability assessment.
  • Utilization of a deep Q-learning agent for agricultural futures market price forecasting.
  • Integration of agri-environmental indicators (AEIs) and edge computing for efficient data processing.

Main Results:

  • The MAS effectively supports users in selecting suppliers based on sustainability preferences.
  • The deep Q-learning agent provides insights into agricultural futures market price fluctuations.
  • The system optimizes price setting and user information access.

Conclusions:

  • The developed MAS enhances the purchasing of sustainable agricultural products.
  • The system offers valuable insights into market dynamics and supplier sustainability.
  • This approach addresses challenges in food security, efficiency, and environmental sustainability in agriculture.