Jove
Visualize
Contact Us

Related Concept Videos

Short-distance Transport of Resources02:12

Short-distance Transport of Resources

Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
Application of Differentiation to Business01:29

Application of Differentiation to Business

Calculus offers essential techniques for businesses seeking to optimize pricing strategies and revenue. In this case, a bakery wants to determine the ideal price and daily sales volume to maximize revenue. By modeling how changes in price affect demand and revenue, the bakery can apply calculus to make data-driven decisions.The demand function relates the price per cupcake to the number of cupcakes sold and captures how lower prices increase sales. Based on market data, the demand function can...

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Ma et al. A Lightweight, Low-Frequency, Broadband Underwater Acoustic Transducer with Ternary Symmetric Excitation: Integrating KNN and Terfenol-D for Enhanced Performance. <i>2026</i>, <i>26</i>, 3645.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

Sensors (Basel, Switzerland)·2026
Same journal

Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.

Sensors (Basel, Switzerland)·2026
Same journal

FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.

Sensors (Basel, Switzerland)·2026
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 Experiment Video

Updated: Jul 19, 2026

A Gradient-generating Microfluidic Device for Cell Biology
11:05

A Gradient-generating Microfluidic Device for Cell Biology

Published on: August 30, 2007

15.3K

Deep Deterministic Policy Gradient-Based Resource Allocation Considering Network Slicing and Device-to-Device

Hudson Henrique de Souza Lopes1, Lucas Jose Ferreira Lima1, Telma Woerle de Lima Soares2

  • 1Electrical, Mechanical and Computer (EMC) School of Engineering, Federal University of Goias (UFG), Goiânia 74605010, GO, Brazil.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary

This study introduces DDPG-KRP, a deep reinforcement learning method for dynamic resource allocation in next-generation mobile networks. It efficiently manages network slices and device-to-device communications, outperforming other algorithms.

Keywords:
deep reinforcement learningdevice-to-devicenetwork slicingresource allocation

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
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

494

Related Experiment Videos

Last Updated: Jul 19, 2026

A Gradient-generating Microfluidic Device for Cell Biology
11:05

A Gradient-generating Microfluidic Device for Cell Biology

Published on: August 30, 2007

15.3K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
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

494

Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Next-generation mobile networks (B5G/6G) require diverse resource management.
  • Network slicing (NS) and device-to-device (D2D) communication are key technologies for efficiency.
  • Integrating NS and D2D can enhance spectrum utilization and network performance.

Purpose of the Study:

  • To address dynamic resource allocation challenges in wireless networks with NS and D2D.
  • To develop an efficient deep reinforcement learning (DRL) approach for this complex problem.
  • To optimize resource allocation policies for maximizing long-term network rewards.

Main Methods:

  • Proposed DDPG-KRP: a DRL approach combining Deep Deterministic Policy Gradient (DDPG), K-Nearest Neighbors (KNNs), and Reward Penalization (RP).
  • DDPG-KRP determines resource allocation policies by maximizing cumulative rewards.
  • RP is used to eliminate undesirable actions, refining the DRL agent's decision-making process.

Main Results:

  • DDPG-KRP demonstrated efficiency in dynamic resource allocation for wireless networks with slicing.
  • The proposed method outperformed other DRL algorithms in simulation experiments.
  • Effective management of resources for combined NS and D2D communications was achieved.

Conclusions:

  • DDPG-KRP offers a robust solution for resource allocation in advanced mobile networks.
  • The integration of DRL, NS, and D2D communication presents a promising direction for future network optimization.
  • This approach enhances scalability and performance in diverse network environments.