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

Horizontal Curve: Problem Solving01:03

Horizontal Curve: Problem Solving

99
A horizontal curve is characterized by its radius, intersection angle, and stationing of key points. In this case, the radius is 400 meters, and the angle of intersection is 30 degrees, with the station of the point of curvature (P.C.) at 0 + 150 meters. The goal is to determine the station values at the point of intersection (P.I.), point of tangency (P.T.), and midpoint of the curve, as well as the length of the long chord.The process begins with calculating the tangent distance (T) and the...
99
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

565
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
565
Elevation of Intermediate Points on Vertical Curves01:20

Elevation of Intermediate Points on Vertical Curves

67
Vertical curves are essential in roadway design because they provide smooth transitions between varying roadway grades. Designing vertical curves involves calculating intermediate elevations and identifying the curve's highest or lowest point, which is essential for optimal roadway performance.Intermediate elevations on a vertical curve are determined using the tangent offset method. This method considers the initial elevation at the start of the curve, the grades, and the curve's geometry. The...
67
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

112
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
112
Introduction to Horizontal Curves01:19

Introduction to Horizontal Curves

189
Horizontal curves are essential in highway and railroad design, ensuring smooth and safe transitions between straight path segments, or tangents. These curves allow vehicles to maintain speed without abrupt changes, minimizing accidents and improving travel efficiency.A horizontal curve is typically defined by its geometric relationship to two tangents that meet at an intersection point (P.I.), where a simple curve is introduced to connect them. The back tangent refers to the initial tangent...
189
Introduction to Vertical Curves01:24

Introduction to Vertical Curves

136
Vertical curves are parabolic transitions that connect different grades on highways and railroads, ensuring a smooth alignment between back and forward tangents. The back tangent represents the initial grade, while the forward tangent defines the subsequent grade. These curves can be symmetrical, with equal tangent lengths, or nonsymmetrical, with varying lengths. The key points defining a vertical curve include the Point of Vertical Intersection (P.V.I.), where the tangents meet; the Point of...
136

You might also read

Related Articles

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

Sort by
Same author

Association of Macrophage Migration Inhibitory Factor (MIF) with Therapy Response and Clinical Outcomes in HPV-Related Head and Neck Squamous Cell Carcinoma: A Preliminary Report.

Current oncology (Toronto, Ont.)·2026
Same author

Bee-inspired navigation robot pinpoints its home using a neural network.

Nature·2026
Same author

A framework for constructing insect steering circuits.

PLoS computational biology·2026
Same author

The adaptor protein TASL is required for age-related B cell emergence and lupus-like disease development in mice.

PLoS biology·2026
Same author

The neurobiology of bee dance communication.

Current opinion in neurobiology·2026
Same author

The role of routine imaging in identifying endoluminal colorectal pathology, a United Kingdom clinical experience.

Abdominal radiology (New York)·2025

Related Experiment Video

Updated: Aug 24, 2025

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
08:04

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

4.5K

Weighted cue integration for straight-line orientation.

Shahrzad Shaverdian1, Elin Dirlik1, Robert Mitchell2

  • 1Lund Vision Group, Department of Biology, Lund University, 223 62 Lund, Sweden.

Iscience
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

Dung beetles integrate multiple environmental cues, like an ersatz sun and wind, to navigate. Their behavior is best explained by continuously combining these cues as a vector sum, not by switching between them.

Keywords:
Biological sciencesEthologyZoology

More Related Videos

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.7K
Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.2K

Related Experiment Videos

Last Updated: Aug 24, 2025

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
08:04

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

4.5K
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.7K
Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.2K

Area of Science:

  • Animal behavior
  • Sensory integration
  • Insect navigation

Background:

  • Animals often integrate multiple sensory cues for behavior.
  • Previous insect studies suggest cue reliability influences weighting, but integration strategies remain unclear.

Purpose of the Study:

  • To characterize the directional reliability of ersatz sun and wind cues for dung beetles.
  • To determine how cue reliability influences cue dominance in conflicting situations.
  • To model the integration strategy used by dung beetles.

Main Methods:

  • Assessed directional reliability of ersatz sun at varying elevations and wind at different speeds.
  • Presented conflicting cues to observe dominance.
  • Developed computational models to test integration strategies (vector sum vs. switching).

Main Results:

  • Relative cue reliability determined dominance when cues conflicted.
  • Behavior was best explained by continuous vector-sum integration, not cue switching.
  • Observed non-optimal cue weighting and minor individual biases.

Conclusions:

  • Dung beetles use continuous vector-sum integration for multiple cues.
  • Insect central complex neural circuitry supports this integration mechanism.
  • Behavioral integration is complex, involving non-optimal weighting and individual biases.