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

Modeling and Similitude01:12

Modeling and Similitude

366
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
366
Typical Model Studies01:30

Typical Model Studies

457
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
457
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

184
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
184
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

137
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...
137
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

278
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
278
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.3K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Severe bottleneck of ancient Homo populations: Insights from computational modeling and relevant fossil evidence.

Molecular biology and evolution·2026
Same author

Uncoupling the nexus between yield and carotenoid levels in sweet potato: development of improved cultivars and identification of key improvement genes.

BMC plant biology·2026
Same author

Carbon-based composite for treatment of chromium contaminated soil: Performance and mechanisms.

Environmental geochemistry and health·2026
Same author

Wastewater-based Surveillance of <i>Salmonella</i> Senftenberg as an Early-warning Indicator for Foodborne Outbreaks - Lianyungang City, Jiangsu Province, China, 2023-2025.

China CDC weekly·2026
Same author

Synergistic Suppression of Secondary Electron Yield from Al<sub>2</sub>O<sub>3</sub> Ceramic Windows by TiN Film and Laser Surface Texturing.

Nanomaterials (Basel, Switzerland)·2026
Same author

Heart-brain axis dysregulation in PTSD mice: Vagal-mediated insular cortex hyperactivity and its reversal by propranolol.

European journal of pharmacology·2026

Related Experiment Video

Updated: Oct 3, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

644

Process knowledge graph modeling techniques and application methods for ship heterogeneous models.

Jianwei Dong1, Xuwen Jing1, Xiang Lu2

  • 1School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, People's Republic of China.

Scientific Reports
|February 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel process knowledge representation model for ship heterogeneous models. It enables efficient reuse of design experience, accelerating ship component assembly and welding process design.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.4K

Related Experiment Videos

Last Updated: Oct 3, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

644
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.4K

Area of Science:

  • Marine Engineering
  • Knowledge Representation
  • Computer-Aided Design

Background:

  • Heterogeneous models in marine component design hinder process knowledge and experience reuse.
  • Existing methods struggle to effectively express and leverage embedded design knowledge.

Purpose of the Study:

  • To propose a process knowledge representation model for ship heterogeneous models.
  • To enable efficient expression and reuse of process design knowledge and experience.
  • To facilitate rapid process design for marine components.

Main Methods:

  • Construction of a multi-element process knowledge graph for unified description of heterogeneous ship models.
  • Application of multi-strategy ontology mapping for semantic expression between knowledge graphs and entity models.
  • Case-based reasoning for implicit semantics acquisition and similarity matching to achieve knowledge reuse.

Main Results:

  • Successfully unified the description of heterogeneous ship models.
  • Achieved semantic expression between process knowledge graphs and entity models.
  • Demonstrated effective case knowledge reuse for rapid process design, validated with a ship's double-deck bottom segment example.

Conclusions:

  • The proposed model effectively acquires process knowledge from design cases.
  • It significantly improves the efficiency and intelligence of knowledge reuse in ship heterogeneous model process design.
  • Provides reliable technical support for ship component assembly and welding, shortening design cycles and enhancing efficiency.