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

Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.3K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.3K
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.1K
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
1.1K
Inverse Trigonometric Functions01:29

Inverse Trigonometric Functions

283
Inverse trigonometric functions are fundamental mathematical tools that reverse the actions of standard trigonometric functions. While trigonometric functions map angles to ratios, inverse trigonometric functions perform the opposite operation by mapping a ratio back to its corresponding angle. These functions are essential in various applications, particularly in determining angles when given specific distances, such as calculating elevation angles in navigation and engineering.For a function...
283
Sampling Plans01:23

Sampling Plans

993
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
993
Inverse Hyperbolic Functions and Their Derivatives01:25

Inverse Hyperbolic Functions and Their Derivatives

80
The shape of a suspension bridge cable hanging under its own weight is described by a catenary curve, which is modeled using the hyperbolic cosine function. This mathematical model accurately captures the balance between gravity and tension acting along the cable. When a particular vertical position on the cable is known, the corresponding horizontal position can be determined using the inverse hyperbolic cosine function, allowing for a detailed analysis of the cable's geometry.Inverse...
80
Derivatives of Inverse Trigonometric Functions01:30

Derivatives of Inverse Trigonometric Functions

429
A ship tracking an approaching aircraft relies on geometric measurements to find out the aircraft’s position relative to the observer. By measuring the slant distance to the aircraft and the angle of elevation, the horizontal and vertical components of the distance can be obtained using trigonometric relationships. This geometric approach provides a basis for analyzing how the observed angle changes as the aircraft moves closer to the ship.To examine the mathematical behavior of the angle...
429

You might also read

Related Articles

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

Sort by
Same author

Stem-like precursors of exhausted Th cells upheld by a Tox-Myb-Eomes transcriptional hierarchy propagate Th cell responses in chronic infection.

Immunity·2026
Same author

Trading robustness: A scenario-free approach to robust multi-criteria optimization for treatment planning.

Medical physics·2026
Same author

High-dimensional multiomics reveals perturbations to IL-6/IL-6R axis and RUNX3 in CD4<sup>+</sup> T cells during third-trimester pregnancy.

Clinical & translational immunology·2026
Same author

High-dimensional multiomics reveals perturbations to IL-6/IL-6R axis and RUNX3 in CD4<sup>+</sup> T cells during third trimester pregnancy.

bioRxiv : the preprint server for biology·2026
Same author

IFN-gene signatures in B cells following influenza A and B virus infection and influenza vaccination.

EMBO molecular medicine·2026
Same author

Chronic Rhinosinusitis Optimisation of Nasal Outcomes and Scores (CHRONOS): An Italian Delphi Consensus on Long-Term Management with Biologics.

Current allergy and asthma reports·2026
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

16.0K

Multi-Criteria Inverse Robustness in Radiotherapy Planning Using Semidefinite Programming.

Jan Schröeder1, Yair Censor2, Philipp Süss1

  • 1Optimization Department, Fraunhofer ITWM, Fraunhofer-Platz 1, Kaiserslautern, 67663, Germany.

Arxiv
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a quantitative method for radiotherapy planning under uncertainty. It balances multiple objectives and robustness using interval matrices and inverse robustness, optimizing treatment plans effectively.

Keywords:
ClusteringIMRTInterval MatricesInterval UncertaintyPareto-FrontRadiotherapy PlanningRobustness

More Related Videos

Voluntary Breath-hold Technique for Reducing Heart Dose in Left Breast Radiotherapy
11:38

Voluntary Breath-hold Technique for Reducing Heart Dose in Left Breast Radiotherapy

Published on: July 3, 2014

47.5K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

2.0K

Related Experiment Videos

Last Updated: Feb 7, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

16.0K
Voluntary Breath-hold Technique for Reducing Heart Dose in Left Breast Radiotherapy
11:38

Voluntary Breath-hold Technique for Reducing Heart Dose in Left Breast Radiotherapy

Published on: July 3, 2014

47.5K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

2.0K

Area of Science:

  • Medical Physics
  • Optimization Theory
  • Radiation Oncology

Background:

  • Radiotherapy planning involves complex multi-criteria optimization.
  • Uncertainty in treatment parameters poses significant challenges.
  • Balancing objectives and robustness is crucial for effective treatment.

Purpose of the Study:

  • To develop a quantitative approach for radiotherapy planning under uncertainty.
  • To integrate robustness against uncertainty as an objective.
  • To address practical challenges in treatment plan optimization.

Main Methods:

  • Modeling uncertainty using interval matrices derived from dose-influence matrices.
  • Introducing inverse robustness as an objective to maximize uncertainty set volume.
  • Employing a multi-criteria optimization framework.
  • Solving the quadratically constrained quadratic optimization problem (QCQP) via semidefinite programming (SDP) relaxation.

Main Results:

  • Demonstrated a method to handle uncertainty in radiotherapy planning.
  • Successfully integrated inverse robustness into the optimization process.
  • Developed a technique to solve QCQP by transforming it into an SDP.

Conclusions:

  • The proposed quantitative approach effectively balances multiple objectives and uncertainty in radiotherapy planning.
  • Inverse robustness provides a novel way to manage uncertainty.
  • The SDP relaxation method offers a viable solution for complex optimization problems in radiation oncology.