Jove
Visualize
Contact Us

Related Concept Videos

Stream Function01:20

Stream Function

2.1K
In two-dimensional incompressible fluid flow, the continuity equation is essential for ensuring mass conservation, meaning that any change in fluid entering or exiting a region is balanced by a corresponding change elsewhere. For incompressible flow, where density remains constant, this requirement simplifies to the condition that the divergence of the velocity field must be zero. Mathematically, this is expressed as,
2.1K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

4.7K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
4.7K
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

753
Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
753
Gene-Environment Interactions01:20

Gene-Environment Interactions

1.2K
Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
1.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.5K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.5K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.0K

You might also read

Related Articles

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

Sort by
Same author

PSgANet: Polar Sequence-Guided Attention Network for Edge-Related Defect Classification in Contact Lenses.

Sensors (Basel, Switzerland)·2026
Same author

Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach.

Materials (Basel, Switzerland)·2023
Same author

Center Deviation Measurement of Color Contact Lenses Based on a Deep Learning Model and Hough Circle Transform.

Sensors (Basel, Switzerland)·2023
Same author

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network.

Sensors (Basel, Switzerland)·2022
Same author

Three-dimensional image acquisition and reconstruction system on a mobile device based on computer-generated integral imaging.

Applied optics·2017
Same author

Integral imaging system using an adaptive lens array.

Applied optics·2016
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: Jan 31, 2026

Multi-Stream Perfusion Bioreactor Integrated with Outlet Fractionation for Dynamic Cell Culture
10:00

Multi-Stream Perfusion Bioreactor Integrated with Outlet Fractionation for Dynamic Cell Culture

Published on: July 20, 2022

2.8K

Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment.

Bunrong Leang1, Sokchomrern Ean, Ga-Ae Ryu

  • 1Department of Computer Science, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea. bunrongleang@chungbuk.ac.kr.

Sensors (Basel, Switzerland)
|January 6, 2019
PubMed
Summary

This study introduces a Hadoop ecosystem for manufacturing Big Data. It enhances data storage, real-time processing, and security using Apache Hadoop, Kafka, and Spark with public-key cryptography.

Keywords:
Hadoop ecosystemdata processingdata streamingpublic-key cryptographyreal-time analysissecured PLC sensing data

More Related Videos

Construction of an Improved Multi-Tetrode Hyperdrive for Large-Scale Neural Recording in Behaving Rats
10:04

Construction of an Improved Multi-Tetrode Hyperdrive for Large-Scale Neural Recording in Behaving Rats

Published on: May 9, 2018

11.8K
Bioindication Testing of Stream Environment Suitability for Young Freshwater Pearl Mussels Using In Situ Exposure Methods
07:53

Bioindication Testing of Stream Environment Suitability for Young Freshwater Pearl Mussels Using In Situ Exposure Methods

Published on: September 5, 2018

7.8K

Related Experiment Videos

Last Updated: Jan 31, 2026

Multi-Stream Perfusion Bioreactor Integrated with Outlet Fractionation for Dynamic Cell Culture
10:00

Multi-Stream Perfusion Bioreactor Integrated with Outlet Fractionation for Dynamic Cell Culture

Published on: July 20, 2022

2.8K
Construction of an Improved Multi-Tetrode Hyperdrive for Large-Scale Neural Recording in Behaving Rats
10:04

Construction of an Improved Multi-Tetrode Hyperdrive for Large-Scale Neural Recording in Behaving Rats

Published on: May 9, 2018

11.8K
Bioindication Testing of Stream Environment Suitability for Young Freshwater Pearl Mussels Using In Situ Exposure Methods
07:53

Bioindication Testing of Stream Environment Suitability for Young Freshwater Pearl Mussels Using In Situ Exposure Methods

Published on: September 5, 2018

7.8K

Area of Science:

  • * Computer Science
  • * Industrial Engineering

Background:

  • * Increasing volumes of sensor data from Programmable Logic Controllers (PLCs) in manufacturing necessitate robust Big Data platforms.
  • * Existing data management solutions struggle to efficiently handle large-scale, real-time manufacturing data.
  • * The need for secure and scalable data infrastructure in smart manufacturing environments is critical.

Purpose of the Study:

  • * To propose and evaluate a Hadoop-based ecosystem tailored for manufacturing Big Data challenges.
  • * To integrate Apache Hadoop, HBase, Kafka, and Spark for comprehensive data management.
  • * To implement public-key cryptography for secure data transmission within the ecosystem.

Main Methods:

  • * Implementation of Apache Hadoop and HBase for large-scale Big Data storage.
  • * Utilization of Apache Kafka as a data streaming pipeline with configurations for scalability (e.g., Kafka offset and partition).
  • * Integration of Apache Spark for real-time data processing and analysis in conjunction with Kafka consumers.
  • * Application of public-key cryptography for securing data transmission between Kafka producers and consumers.

Main Results:

  • * The proposed Hadoop ecosystem effectively handles large-scale PLC sensing data from manufacturing environments.
  • * Real-time data processing and analysis are achieved through the synergy of Kafka and Spark.
  • * Public-key cryptography ensures secure data transmission, protecting sensitive manufacturing data.
  • * The integrated system demonstrates enhanced performance in data storing, processing, and security.

Conclusions:

  • * The developed Hadoop ecosystem provides a scalable and reliable solution for Big Data in manufacturing.
  • * The combination of Big Data technologies and cryptographic methods significantly improves data management capabilities.
  • * This approach enhances the overall efficiency, accuracy, and security of manufacturing data operations.