Jove
Visualize
Contact Us

Related Concept Videos

Coordination Number and Geometry02:57

Coordination Number and Geometry

19.0K
For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
19.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
Geometry of Hyperbolas01:30

Geometry of Hyperbolas

501
A hyperbola consists of all points where the absolute difference of distances to two fixed points, called foci, remains constant. The standard equation isEach branch extends infinitely and approaches two asymptotes, which guide the curve’s behavior. The parameters a and b define key features: a measures the distance from the center to each vertex along the transverse axis, while b influences the slopes of the asymptotes. The asymptotes have equationsA rectangle centered at the origin with...
501
Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

19.0K
The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
19.0K
Radicals: Electronic Structure and Geometry01:07

Radicals: Electronic Structure and Geometry

5.1K
This lesson delves into the geometry of a radical, which is influenced by the electronic structure of the molecule. The principle is similar to that of a lone pair, where the unpaired electron influences the geometry at the radical center.
Accordingly, the structure of a trivalent radical lies between the geometries of carbocations and carbanions. An sp2-hybridized carbocation is trigonal planar, while an sp3-hybridized carbanion is trigonal pyramidal. Here, the difference in geometry is...
5.1K
Optimal Foraging00:48

Optimal Foraging

13.8K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.8K

You might also read

Related Articles

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

Sort by
Same author

Gentrius: Generating Trees Compatible With a Set of Unrooted Subtrees and its Application to Phylogenetic Terraces.

Molecular biology and evolution·2024
Same author

Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic.

PLOS global public health·2023
Same author

It Is Just a Matter of Time: Balancing Homologous Recombination and Non-homologous End Joining at the rDNA Locus During Meiosis.

Frontiers in plant science·2021
Same author

Sequencing of the Arabidopsis NOR2 reveals its distinct organization and tissue-specific rRNA ribosomal variants.

Nature communications·2021
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 Experiment Video

Updated: Feb 3, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K

Geometry of distribution-constrained optimal stopping problems.

Mathias Beiglböck1, Manu Eder2, Christiane Elgert2

  • 11Faculty of Mathematics, Vienna University, Oskar Morgensternplatz 1, 1090 Vienna, Austria.

Probability Theory and Related Fields
|November 6, 2018
PubMed
Summary
This summary is machine-generated.

This study uses optimal transport theory to geometrically describe optimal stopping times for Brownian motion with a specified distribution. The findings reveal solutions often involve hitting a barrier in a phase space, connecting to inverse first passage time problems.

Keywords:
Distribution-constrained optimal stoppingInverse first passage problemOptimal transportShiryaev’s problem

More Related Videos

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.6K
Control of Cell Geometry through Infrared Laser Assisted Micropatterning
11:04

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Published on: July 10, 2021

3.9K

Related Experiment Videos

Last Updated: Feb 3, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.6K
Control of Cell Geometry through Infrared Laser Assisted Micropatterning
11:04

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Published on: July 10, 2021

3.9K

Area of Science:

  • Stochastic Calculus
  • Probability Theory
  • Optimal Control

Background:

  • Optimal stopping problems involve determining the best time to take a certain action to maximize or minimize a reward.
  • Brownian motion is a fundamental stochastic process used to model random movements.
  • Optimal transport theory provides tools to move probability distributions efficiently.

Purpose of the Study:

  • To develop a geometric description of optimal stopping times for Brownian motion.
  • To incorporate a constraint on the distribution of the stopping time.
  • To adapt optimal transport concepts to stochastic processes.

Main Methods:

  • Application of optimal transport and martingale optimal transport concepts.
  • Geometric analysis of stopping times.
  • Analysis of cost processes adapted to the Brownian filtration.

Main Results:

  • A geometric framework for optimal stopping times with distributional constraints.
  • Identification of solutions as first hitting times of barriers in phase space for various cost processes.
  • Recovery of classical solutions to inverse first passage time and Shiryaev's problems.

Conclusions:

  • Optimal transport provides powerful geometric insights into optimal stopping problems.
  • The hitting time in phase space is a prevalent solution structure.
  • The methods offer a unified approach to related problems in stochastic analysis.