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

PD Controller: Design01:26

PD Controller: Design

744
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
744
Behavior Modification01:21

Behavior Modification

976
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
976
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

688
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
688
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.4K
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...
7.4K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.9K
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...
5.9K
Modeling in Therapy01:26

Modeling in Therapy

760
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
760

You might also read

Related Articles

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

Sort by
Same author

Silencing of human RNA polymerase I subunit A34 influences multiple cellular processes including rDNA transcription and cell migration.

International journal of biological macromolecules·2026
Same author

Game, Set, and Match: A Scoping Review of Matching Characteristics for Control and Intervention Groups in Adaptive Behavioral Interventions for Physical Activity or Healthy Eating Designs for Populations with Overweight and Obesity.

Behavioral medicine (Washington, D.C.)·2026
Same author

Revisiting Direct Sibling Influence on Eating Behavior in Early Childhood.

Academic pediatrics·2026
Same author

Predicting structure- and control-based parental feeding practices among families with low-income: An ecological approach.

Appetite·2026
Same author

Identification of PD-1-PD-L1 blockade epitopes in vitro utilizing porcine immunoglobulin and heterologous Fc-fused protein.

Veterinary research·2025
Same author

An Optimized Behavioral Intervention for Managing Gestational Weight Gain Using Semi-Physical Modeling and Hybrid Model Predictive Control.

IEEE International Conference on Communications : [proceedings]. IEEE International Conference on Communications·2025

Related Experiment Video

Updated: Apr 18, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.3K

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

Yuwen Dong1, Sunil Deshpande1, Daniel E Rivera1

  • 1Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA.

Proceedings of the ... American Control Conference. American Control Conference
|January 31, 2015
PubMed
Summary

Control engineering optimizes adaptive behavioral interventions using a novel Mixed Logical Dynamical (MLD) framework extension. This approach enhances sequential decision-making for personalized treatment policies, improving intervention effectiveness.

Related Experiment Videos

Last Updated: Apr 18, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.3K

Area of Science:

  • Control Engineering
  • Behavioral Science
  • Health Informatics

Background:

  • Adaptive behavioral interventions require precise, timely policy adjustments.
  • Existing Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes have limitations in handling sequential decision-making for these interventions.

Purpose of the Study:

  • To extend the MLD framework for HMPC to accommodate sequential decision policies in adaptive behavioral interventions.
  • To represent sequential decision requirements as mixed-integer linear constraints within the HMPC framework.

Main Methods:

  • Developed an extension of the MLD framework for HMPC.
  • Incorporated user-specified dosage sequence tables and a switching time strategy.
  • Represented sequential decision policies as mixed-integer linear constraints.

Main Results:

  • Successfully generated sequential decision policies for adaptive behavioral interventions.
  • Demonstrated the effectiveness of the extended MLD-HMPC framework using a gestational weight gain (GWG) intervention model.
  • Showcased the ability to assign dosages at intervals less frequent than the measurement sampling interval.

Conclusions:

  • The extended MLD-HMPC framework effectively addresses sequential decision-making requirements for adaptive behavioral interventions.
  • This approach offers a systematic and efficient method for optimizing individually tailored treatment and prevention policies.
  • The model provides a robust tool for implementing complex, multi-component adaptive interventions.