Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Manipulation and Analysis

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

Hydraulic Jump: Problem Solving

Hydraulic Jump: Problem Solving

To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...

Design Example: Creating a Hydraulic Model of a Dam Spillway

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.

Selected Data About Geographic Locations

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

Modeling and Similitude

Modeling and Similitude

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...

Design Example: Alignment of a Road Line Using GIS

Design Example: Alignment of a Road Line Using GIS

The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...

You might also read

Related Articles

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

Sort by

Same author

Allograft inflammatory factor-1 in macrophages promotes renal tubular cell epithelial-mesenchymal transition via the NF-κB/IL-1β axis in diabetic kidney disease.

Biochemical and biophysical research communications·2026

Same author

Optical cooling by interfacial charge transfer in 2D heterostructures.

Nature·2026

Same author

Tectonic controls governing reservoir heterogeneity within a stepped carbonate platform of the Sinian Dengying Formation in the Sichuan Basin, China.

Scientific reports·2026

Same author

Multiproxy Geochemical Reconstruction of Paleoenvironment and Its Control on Fine-Grained Sedimentation in the Da'anzhai Member of the Jurassic System, Sichuan Basin.

ACS omega·2026

Same author

Circ_0001185 as a novel therapeutic target for lupus nephritis according to ceRNA network comprehensive analysis.

Clinical rheumatology·2026

Same author

A practitioner's guide to using data on private equity hospital acquisitions.

Health affairs scholar·2026

Search research articles

Related Experiment Video

Updated: Jul 4, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Knowledge graph completion method for hydraulic engineering coupled with spatial transformation and an attention

Yang Liu¹, Tianran Tao², Xuemei Liu²

¹Provincial Collaborative Innovation Center for Efficient Utilization of Water Resources in the Yellow River Basin, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

Mathematical Biosciences and Engineering : MBE

|February 2, 2024

Summary

This study introduces a new model for knowledge graph embedding that enhances semantic information extraction. The Spatial Transformation and Attention Mechanisms (STAM) model improves link prediction accuracy on benchmark datasets and in hydraulic engineering.

Keywords:

convolutional neural network hydraulic engineering knowledge graph knowledge graph completion multi-scale channel attention mechanism spatial transformation

More Related Videos

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Related Experiment Videos

Last Updated: Jul 4, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

Artificial Intelligence
Data Science
Computer Science

Background:

Knowledge graph completion (KGC) methods struggle with extracting rich semantic information.
Existing models often fail to capture complex relationships between entities and relations.

Purpose of the Study:

To propose a novel model, Spatial Transformation and Attention Mechanisms (STAM), for enhanced knowledge graph embedding.
To improve the accuracy of semantic information extraction in KGC.

Main Methods:

STAM combines spatial transformation and attention mechanisms for knowledge graph embedding.
It utilizes a 2D convolutional neural network and a multi-scale channel attention mechanism.
The model fuses shallow and latent information for richer semantic expression.

Main Results:

STAM improved Mean Reciprocal Rank (MRR) by 8.8% on WN18RR, 10.5% on FB15K237, and 6.9% on Kinship compared to ConvE.
The model demonstrated superior performance in MRR, Hits@1, Hits@3, and Hits@10 on a hydraulic engineering dataset.

Conclusions:

STAM effectively enhances semantic information extraction for knowledge graph completion.
The proposed model shows significant improvements in link prediction accuracy across various datasets, including specialized domains like hydraulic engineering.