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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

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.
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Modeling and Similitude01:12

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 Consideration01:22

Design Consideration

Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key aspect...
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

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.

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Related Experiment Video

Updated: Jun 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Use of Large Language Models to Enhance Failure Mode and Effects Analysis: A Case Study.

Saurabh S Nair1, Laurence Court1, Raphael Douglas1

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Advances in Radiation Oncology
|June 8, 2026
PubMed
Summary

Large language models (LLMs) can help identify new risks in radiation oncology planning, but expert review is crucial for validating these findings. AI tools enhance, not replace, human judgment in safety assessments.

Related Experiment Videos

Last Updated: Jun 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Failure Mode and Effects Analysis (FMEA) is vital for risk mitigation in radiation oncology but is resource-intensive.
  • Traditional FMEA relies heavily on expert experience, potentially limiting the scope of identified risks.

Purpose of the Study:

  • To evaluate the efficacy of large language models (LLMs) in supplementing expert-driven FMEA for identifying novel failure modes.
  • To assess LLM capabilities in uncovering risks within the Radiation Planning Assistant (RPA) workflow.

Main Methods:

  • Four LLMs were used by a multidisciplinary team to generate failure modes for the RPA workflow.
  • LLM-generated failure modes were scored for severity, occurrence, and detectability, then independently rescored by experts using the TG-100 framework.
  • High-risk modes were reviewed by clinical users in South Africa.

Main Results:

  • LLMs generated 190 candidate failure modes, with 79 unique modes identified after review.
  • LLMs tended to assign higher severity and lower detectability scores compared to experts, resulting in higher mean Risk Priority Numbers (RPNs).
  • Clinical users confirmed the plausibility of several AI-identified risks, especially those related to accountability.

Conclusions:

  • LLMs can expand risk identification in FMEA by uncovering previously unrecognized failure modes.
  • Expert oversight is indispensable for validating and prioritizing AI-identified risks.
  • AI should be considered a complementary tool to enhance, not replace, human expertise in radiation therapy safety.