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

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

195
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
195
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

312
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
312
Rapidly Varying Flow01:24

Rapidly Varying Flow

154
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
154
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

760
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...
760
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

367
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
367
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

197
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
197

You might also read

Related Articles

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

Sort by
Same author

Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques.

International journal of environmental research and public health·2023
Same author

Selection of an Efficient Classification Algorithm for Ambient Assisted Living: Supportive Care for Elderly People.

Healthcare (Basel, Switzerland)·2023
Same author

A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques.

Healthcare (Basel, Switzerland)·2022
Same author

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.

Computational intelligence and neuroscience·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 27, 2025

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

673

QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment.

Mohamed Ali Rakrouki1,2,3, Nawaf Alharbe1

  • 1Applied College, Taibah University, Medina 42353, Saudi Arabia.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary
This summary is machine-generated.

This study optimizes task scheduling for reduced energy consumption and improved quality of service (QoS). The novel HPSO algorithm enhances resource efficiency and minimizes service-level agreement (SLA) violations.

Keywords:
ARIMAQoScloud computingschedulingvirtual machine placement

More Related Videos

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K
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

8.2K

Related Experiment Videos

Last Updated: Sep 27, 2025

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

673
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K
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

8.2K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Efficient task scheduling is crucial for managing computational resources and meeting user demands.
  • Minimizing energy consumption in physical machines is a key challenge in modern data centers.
  • Ensuring Quality of Service (QoS) requirements are met is essential for user satisfaction.

Purpose of the Study:

  • To develop an optimized task scheduling model that reduces energy consumption while adhering to QoS requirements.
  • To improve the accuracy of QoS prediction for dynamic task scheduling.
  • To enhance the efficiency of scheduling algorithms through hybridization.

Main Methods:

  • Utilized an ARIMA prediction model optimized with a Kalman filter for QoS prediction.
  • Developed a hybrid scheduling policy combining Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA).
  • Proposed the HPSO algorithm for task scheduling based on QoS status analysis.

Main Results:

  • The HPSO algorithm demonstrated a 16.51% greater reduction in resource consumption compared to the original hybrid algorithm.
  • The optimized prediction model led to a 0.053% reduction in Service Level Agreement (SLA) violations.
  • The proposed method effectively balances energy efficiency and QoS adherence.

Conclusions:

  • The integrated approach of optimized QoS prediction and hybrid PSO-GSA scheduling significantly enhances system performance.
  • The HPSO algorithm offers a superior solution for energy-aware task scheduling in complex computing environments.
  • Accurate QoS prediction is vital for effective resource management and SLA compliance.