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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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...
Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Introduction to Scalers01:21

Introduction to Scalers

Many familiar physical quantities can be specified completely by giving a single number and the appropriate unit. For example, "a class period lasts 50 min," or "the gas tank in my car holds 65 L," or "the distance between the two posts is 100 m." A physical quantity that can be specified completely in this manner is called a scalar quantity. The word "scalar" is a synonym for "number." Time, mass, distance, length, volume, temperature, and energy are some examples of scalar quantities.
Scalar...

You might also read

Related Articles

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

Sort by
Same author

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge.

Journal of computational biology : a journal of computational molecular cell biology·2025
Same author

PSI/J: A Portable Interface for Submitting, Monitoring, and Managing Jobs.

Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience·2025
Same author

The need to implement FAIR principles in biomolecular simulations.

Nature methods·2025
Same author

Pathway-based analyses of gene expression profiles at low doses of ionizing radiation.

Frontiers in bioinformatics·2024
Same author

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics.

The international journal of high performance computing applications·2024
Same author

Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC.

Nature communications·2024
Same journal

Inverse FIP effect plasma in the solar atmosphere: a synthesis of current understanding and new insights from AR 11967.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Signs of sulfur fractionation under high magnetic field strength.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

First ionization potential fractionation of sulfur observed with spectral imaging of the coronal environment.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Chromospheric dynamics and turbulence regulate the solar FIP effect.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Exploring the link between wave activity in the photospheric velocity driver and the FIP bias in the solar corona.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Radiative hydrodynamic simulations of first ionization potential fractionation in solar flares.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Understanding performance of distributed data-intensive applications.

Christopher Miceli1, Michael Miceli, Bety Rodriguez-Milla

  • 1Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|August 4, 2010
PubMed
Summary
This summary is machine-generated.

This study explores performance factors for data-intensive applications on cloud infrastructures. It introduces a genome sequence matching application to evaluate data placement strategies for improved scalability and interoperability.

More Related Videos

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Related Experiment Videos

Last Updated: Jun 10, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Area of Science:

  • Computer Science
  • Bioinformatics
  • Distributed Systems

Background:

  • Grids, clouds, and cloud-like infrastructures support data-intensive applications.
  • Increasing data volume and distribution introduce unique performance challenges.
  • Scalable data management and workload placement are critical for performance.

Purpose of the Study:

  • To understand factors influencing the performance of data-intensive applications.
  • To prototype and study a genome sequence matching application as a data-intensive model.
  • To demonstrate the extensibility and interoperability of the SAGA approach across infrastructures.

Main Methods:

  • Developed a genome sequence matching application using a SAGA-based implementation of the All-Pairs pattern.
  • Studied performance factors influencing the application.
  • Compared application-level data-placement heuristics with distributed file systems for execution.

Main Results:

  • Identified key factors affecting the performance of distributed data-intensive applications.
  • Demonstrated the SAGA approach's capability for extensibility and interoperability.
  • Provided insights into the trade-offs between different data placement strategies.

Conclusions:

  • The SAGA approach facilitates data-intensive applications across diverse infrastructures.
  • Understanding data and workload placement is crucial for optimizing performance.
  • This work offers a foundation for developing more efficient distributed data management techniques.