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

General Characteristics of Pipe Flow I01:22

General Characteristics of Pipe Flow I

1.7K
Pipe flow refers to the movement of fluids within fully enclosed conduits, typically cylindrical in shape, such as water pipes or hydraulic hoses. These conduits are designed to withstand high-pressure gradients that drive fluid movement, contrasting with open-channel flows, where gravity is the primary driving force. Rectangular conduits, like air conditioning and heating ducts, generally operate at lower pressures and are less suited for high-pressure applications.
The classification of fluid...
1.7K
Major Losses in Pipes01:28

Major Losses in Pipes

1.9K
When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to viscous...
1.9K
Minor Losses in Pipes01:25

Minor Losses in Pipes

1.9K
In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
1.9K
Single Pipe Systems01:24

Single Pipe Systems

444
In pipe flow analysis, problems are typically categorized into three types — Type I, Type II, and Type III — based on the known parameters and the desired outcome. Each type of problem addresses specific engineering requirements using fluid properties, pipe characteristics, and operational conditions.
In a Type I problem, fluid properties (density and viscosity), pipe characteristics (including diameter, length, and surface roughness), and the flow rate or average velocity are...
444
Multiple Pipe Systems01:21

Multiple Pipe Systems

1.2K
Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
1.2K
Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

1.2K
In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
The orifice meter is a simple,...
1.2K

You might also read

Related Articles

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

Sort by
Same author

MRI-based measurement of masseter muscle area: reliability and clinical relevance in acute neck infections.

European radiology·2026
Same author

MRI edema patterns in acute neck infections: A multicenter and multidisciplinary interobserver study.

European journal of radiology·2025
Same author

MRI-based risk factors for intensive care unit admissions in acute neck infections.

European journal of radiology open·2025
Same author

Correction: "Prompt Engineering an Informational Chatbot for Education on Mental Health Using a Multiagent Approach for Enhanced Compliance With Prompt Instructions: Algorithm Development and Validation".

JMIR AI·2025
Same author

Prompt Engineering an Informational Chatbot for Education on Mental Health Using a Multiagent Approach for Enhanced Compliance With Prompt Instructions: Algorithm Development and Validation.

JMIR AI·2025
Same author

Odontogenic neck abscesses caused by Streptococcus anginosus group bacteria: emergency MRI findings.

Oral surgery, oral medicine, oral pathology and oral radiology·2025
Same journal

Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model.

F1000Research·2026
Same journal

Emotional intelligence: An important skill to learn now more than ever.

F1000Research·2026
Same journal

East Mediterranean Lineage of <i>Brucella melitensis</i> in Human Isolates and Milk Samples in Oman Using MLVA-14.

F1000Research·2026
Same journal

Application of K-Means Clustering for Job Applicant Analysis in Construction Firms Using R.

F1000Research·2026
Same journal

The influence of self-esteem and emotional intelligence on addiction to social networks in Peruvian university students.

F1000Research·2026
Same journal

A Bibliometric Analysis of Music's Role in Promoting Well-Being in Health Science Research.

F1000Research·2026
See all related articles

Related Experiment Video

Updated: Jan 25, 2026

Setup and Execution Of the Blindfolded Code Training Exercise
05:25

Setup and Execution Of the Blindfolded Code Training Exercise

Published on: March 29, 2019

9.9K

META-pipe cloud setup and execution.

Aleksandr Agafonov1, Kimmo Mattila2, Cuong Duong Tuan3

  • 1Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.

F1000Research
|May 18, 2019
PubMed
Summary
This summary is machine-generated.

META-pipe offers marine metagenomic data analysis, including gene annotation. This study details using cloud computing to expand access to this powerful bioinformatics service for researchers globally.

Keywords:
AAI federationAmazon Web ServicesApache SparkEGI Federated CloudELIXIRMETA-pipeOpenStackPortability

More Related Videos

Setup and Execution of the Rapid Cycle Deliberate Practice Death Notification Curriculum
04:36

Setup and Execution of the Rapid Cycle Deliberate Practice Death Notification Curriculum

Published on: August 5, 2020

4.7K
Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia
04:34

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia

Published on: February 17, 2023

1.6K

Related Experiment Videos

Last Updated: Jan 25, 2026

Setup and Execution Of the Blindfolded Code Training Exercise
05:25

Setup and Execution Of the Blindfolded Code Training Exercise

Published on: March 29, 2019

9.9K
Setup and Execution of the Rapid Cycle Deliberate Practice Death Notification Curriculum
04:36

Setup and Execution of the Rapid Cycle Deliberate Practice Death Notification Curriculum

Published on: August 5, 2020

4.7K
Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia
04:34

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia

Published on: February 17, 2023

1.6K

Area of Science:

  • Marine biology and bioinformatics
  • Metagenomic data analysis

Background:

  • Marine metagenomic data analysis requires significant computational resources.
  • META-pipe provides comprehensive analysis services, including computationally intensive functional annotation.
  • Current infrastructure limits access to ELIXIR users, necessitating scalable solutions.

Purpose of the Study:

  • To describe the methodology for extending META-pipe's functional analysis to cloud platforms.
  • To ensure a powerful, user-friendly, and maintainable data analysis service.
  • To provide a scalable model for distributed data analysis services.

Main Methods:

  • Implementation of META-pipe's functional annotation on academic and commercial cloud environments.
  • Development of a distributed architecture combining central servers with distributed backends.
  • Execution of computationally intensive jobs on geographically distributed resources.

Main Results:

  • Successful setup and execution of META-pipe's functional analysis on cloud infrastructure.
  • Demonstration of a distributed architecture for handling demanding bioinformatics tasks.
  • Validation of cloud resources for scalable metagenomic data processing.

Conclusions:

  • Cloud computing enables the expansion of META-pipe's services to a wider user base.
  • A distributed architecture is effective for managing computationally intensive bioinformatics analyses.
  • The META-pipe model offers a valuable blueprint for distributed data analysis services in research organizations.