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

Weak Base Solutions03:21

Weak Base Solutions

24.9K
Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...
24.9K
Strong Acid and Base Solutions03:22

Strong Acid and Base Solutions

35.3K
A strong acid is a compound that dissociates completely in an aqueous solution and produces a concentration of hydronium ions equal to the initial concentration of acid. For example, 0.20 M hydrobromic acid will dissociate completely in water and produces 0.20 M of hydronium ions and 0.20 M of bromide ions.
35.3K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Solution Composition During Acid/Base Titrations01:17

Solution Composition During Acid/Base Titrations

1.5K
The titration of a weak acid with a strong base results in the formation of water and the conjugate base of the acid. For instance, titrating acetic acid with sodium hydroxide leads to the formation of water and sodium acetate. A solution of acetic acid and sodium acetate constitutes a buffer whose relative concentration at different stages of the titration is indicated by the α values, which represent percentages of the weak acid and its conjugate base.
The α0 and α1 values...
1.5K
Leveling Effect and Non-Aqueous Acid-Base Solutions02:11

Leveling Effect and Non-Aqueous Acid-Base Solutions

9.4K
This lesson defines the leveling effect in acidic and basic solutions and its role in aqueous and non-aqueous solutions. It is essential to understand the competing nature of various species in a chemical system.
The Leveling Effect of a Solvent
A generic acid (HA) reacts with the generic base (B-) to yield the corresponding conjugate base (A-) and conjugate acid (HB):
9.4K
Parallel Resonance01:23

Parallel Resonance

531
The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
531

You might also read

Related Articles

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

Sort by
Same author

Heterogeneous iron oxide nanoparticles anchored on carbon nanotubes for high-performance lithium-ion storage and fenton-like oxidation.

Journal of colloid and interface science·2021
Same author

Cost-effectiveness analysis of the integrated control strategy for schistosomiasis japonica in a lake region of China: a case study.

Infectious diseases of poverty·2021
Same author

Targeting Gα<sub>13</sub>-integrin interaction ameliorates systemic inflammation.

Nature communications·2021
Same author

Screening and mitigating major threats of regional development to water ecosystems using ecosystem services as endpoints.

Journal of environmental management·2021
Same author

Facile synthesis of a rod-like porous carbon framework confined magnetite nanoparticle composite for superior lithium-ion storage.

Journal of colloid and interface science·2021
Same author

Wafer-Scale and Full-Coverage Two-Dimensional Molecular Monolayers Strained by Solvent Surface Tension Balance.

ACS applied materials & interfaces·2021

Related Experiment Video

Updated: Jan 23, 2026

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

8.5K

Spark-Based Parallel Genetic Algorithm for Simulating a Solution of Optimal Deployment of an Underwater Sensor

Peng Liu1,2, Shuai Ye3, Can Wang4

  • 1National and Local Joint Engineering Laboratory of Internet Application Technology of Mines, Xuzhou 221008, China. liupeng@cumt.edu.cn.

Sensors (Basel, Switzerland)
|June 20, 2019
PubMed
Summary

Optimizing underwater sensor networks with a Spark-based genetic algorithm (GA) significantly reduces deployment time and avoids premature convergence. This approach enhances the efficiency and practicality of large-scale sensor network deployment.

Keywords:
HadoopSparkgenetic algorithmlarge-scale datamulti-peak functionparallel computingunderwater sensor network

More Related Videos

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.6K
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

1.1K

Related Experiment Videos

Last Updated: Jan 23, 2026

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

8.5K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.6K
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

1.1K

Area of Science:

  • Computer Science
  • Engineering
  • Distributed Systems

Background:

  • Large-scale underwater sensor networks face challenges in node deployment due to energy constraints, delays, and disconnections.
  • Genetic algorithms (GA) can optimize deployment but suffer from long computation times for large datasets.
  • Existing parallel frameworks like Hadoop offer some improvement, but Spark provides greater parallel processing capabilities.

Purpose of the Study:

  • To propose and evaluate a Spark-based parallel genetic algorithm (GA) for optimizing underwater sensor network (UWSN) node deployment.
  • To address the limitations of traditional GA in terms of computation time and premature convergence in large-scale UWSN scenarios.
  • To leverage Spark's parallel processing power for efficient GA operations like crossover and mutation.

Main Methods:

  • Developed a Spark-based parallel genetic algorithm (GA) tailored for UWSN environments.
  • Utilized the Shubert multi-peak function to calculate the extremum for optimal sensor deployment.
  • Compared the performance of the Spark-based GA against single-node and Hadoop-based implementations for large-scale UWSN deployment.

Main Results:

  • The Spark-based GA significantly reduced the running time for large-scale UWSN deployment compared to single-node and Hadoop frameworks.
  • The proposed method effectively avoided premature convergence, a common issue in GA, due to enhanced randomness.
  • Optimal deployment of underwater sensor nodes was achieved with improved efficiency.

Conclusions:

  • A Spark-based parallel GA is a highly effective approach for optimizing large-scale underwater sensor network deployment.
  • This method overcomes the computational bottlenecks and convergence issues associated with traditional GA in UWSNs.
  • The Spark implementation offers a practical and efficient solution for real-world UWSN applications.