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

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...
Multiple Pipe Systems01:21

Multiple Pipe Systems

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...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Single Pipe Systems01:24

Single Pipe Systems

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 known. The...
Upstream Processing01:27

Upstream Processing

Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
Microenvironments01:22

Microenvironments

Microorganisms inhabit highly localized spaces known as microenvironments, which are defined by distinct physical and chemical characteristics. These include oxygen concentration, pH, temperature, light availability, and nutrient levels. The conditions within a microenvironment can differ markedly from those in the surrounding area and significantly influence microbial growth, metabolism, and community structure.Microenvironments often display sharp physicochemical gradients over small spatial...

You might also read

Related Articles

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

Sort by
Same author

Association of cerebrovascular center volume with patient outcomes.

Neurosurgical review·2026
Same author

AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Elranatamab Population Pharmacokinetics and Exposure-Response for Cytokine Release Syndrome in Patients with Relapsed or Refractory Multiple Myeloma.

Clinical pharmacokinetics·2026
Same author

Reframing neurotrauma research for LSCO: lessons from Ukraine for the UK Defence Medical Services.

BMJ military health·2026
Same author

Wartime penetrating sellar/parasellar injuries: a novel classification and management based on trajectory.

Journal of neurosurgery·2026
Same author

CORR ® Curriculum-Orthopaedic Education: Can Physician Assistants Replace Orthopaedic Surgeons in the Setting of Resuscitative Surgical Care for Trauma and Combat Casualties?

Clinical orthopaedics and related research·2025
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2026

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

A multi-core CPU pipeline architecture for virtual environments.

Eric Acosta1, Alan Liu, Jennifer Sieck

  • 1The National Capital Area Medical Simulation Center, Uniformed Services University, USA. Eric.Acosta.ctr@simcen.usuhs.edu

Studies in Health Technology and Informatics
|April 21, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a pipeline architecture for virtual environments (VEs) to fully utilize multi-core CPUs in medical simulations. This approach enables complex, realistic simulations to scale efficiently with increasing core counts.

More Related Videos

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality
08:45

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Published on: April 5, 2018

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

Related Experiment Videos

Last Updated: Jun 23, 2026

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality
08:45

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Published on: April 5, 2018

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

Area of Science:

  • Computer Science
  • Medical Simulation
  • High-Performance Computing

Background:

  • Physically-based virtual environments (VEs) are crucial for realistic computer-based medical simulations.
  • Traditional VEs are computationally limited by CPU resources, necessitating simplification for real-time performance.
  • The advent of multi-core processors offers significant computational power but poses challenges for effective utilization in VE development.

Purpose of the Study:

  • To present a novel pipeline VE architecture designed to harness the full potential of multi-core CPU systems.
  • To enable the development of complex, physically-based VEs that can dynamically distribute workloads across all available CPU cores.
  • To create VEs that are scalable and can achieve efficient performance improvements with an increasing number of CPU cores.

Main Methods:

  • Development of a pipeline architecture for virtual environments specifically optimized for multi-core CPU systems.
  • Implementation of dynamic workload distribution mechanisms across available CPU cores for VE simulation.
  • Testing the architecture with a craniotomy simulator to evaluate performance and scalability.

Main Results:

  • Demonstrated a scalable VE architecture capable of leveraging all available CPU cores for simulation.
  • Achieved super-linear and near-linear speedups in performance when utilizing up to four CPU cores.
  • Confirmed the feasibility of developing complex, physically-based VEs for medical simulations using the proposed architecture.

Conclusions:

  • The pipeline VE architecture effectively addresses the challenge of utilizing multi-core processors for enhanced medical simulations.
  • This approach allows for the creation of more sophisticated and realistic VEs without compromising real-time performance.
  • The developed architecture provides a scalable solution for future medical simulation development, benefiting from advancements in multi-core processing.