Fidelity Analysis
Fidelity Analysis - Related Videos
18:10
Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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Cited by 40 •
2011
The present article describes the steps required to isolate and characterize RNA polymerase fidelity variants of RNA viruses and how to use mutation frequency data to confirm fidelity changes in tissue culture.
07:08
A High-Fidelity Porcine Model of Orthotopic Heart Transplantation Following Donation after Circulatory Death
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Cited by 2 •
2025
The protocol here describes a high-fidelity porcine model of heart transplantation following donation after circulatory death utilizing ex vivo perfusion of the allograft.
13:44
A Method for High Fidelity Optogenetic Control of Individual Pyramidal Neurons In vivo
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Cited by 2 •
2013
The recent development of neuroscience tools that combine genetics and optics, termed "optogenetics", enables control over neural circuit activity with an unprecedented level of spatial and temporal resolution. Here we provide a protocol for integrating in vivo recording with optogenetic manipulation of genetically-defined subsets of prefrontal cortical and subicular pyramidal neurons.
11:45
Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing
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2022
We describe a procedure to process computed tomography (CT) scans into high-fidelity, reclaimable, and low-cost procedural task trainers. The CT scan identification processes, export, segmentation, modeling, and 3D printing are all described, along with the issues and lessons learned in the process.
08:03
Building Affordable, Durable, Medium-Fidelity Ballistic Gel Phantoms for Ultrasound-Guided Nerve Block Training
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Cited by 4 •
2024
Here, we demonstrate the design and creation of four custom ballistic gelatin ultrasound phantoms for ultrasound-guided regional anesthesia training. We designed the phantoms using computer-aided design software, used 3D-printed models to create silicone molds, and then poured melted ballistic gel into the molds to create custom tissue layers.
FAQs
Engineers apply comparable inputs and operating conditions to both the representation and the reference system, then compare their outputs. Validation metrics express the size or pattern of discrepancies rather than relying on visual agreement alone. This comparison shows whether the model reproduces behavior closely enough for a particular design decision, performance prediction, or testing objective.
Sensitivity analysis reveals which inputs or conditions most influence differences between modeled and reference behavior. Uncertainty assessment indicates how much confidence engineers should place in those comparisons when outcomes are not exact. Together, they distinguish influential sources of mismatch from less important variation, helping teams prioritize model refinement and judge whether conclusions remain dependable.
A representation does not need identical accuracy for every engineering purpose. Its suitability depends on whether engineers plan to support design decisions, predict performance, conduct testing, or develop a digital twin. Fidelity analysis identifies where behavior is captured well and where it is missed, allowing teams to determine whether the current representation is adequate for its intended use.
The process begins by selecting a model or digital representation and a reference system, then establishing comparable inputs and operating conditions. Engineers measure the resulting outputs, evaluate differences with validation metrics, and examine sensitivity and uncertainty. They use these findings to identify mismatches, refine the representation where necessary, and reassess its suitability for the planned engineering task.
Engineers can use Fidelity Analysis when they need to determine whether a representation is accurate enough for design or performance work before committing to extensive physical experimentation. The comparison reveals where the model is reliable and where experiments or refinement may still be needed. This supports more efficient investigation while retaining direct attention to discrepancies between predicted and reference behavior.
For digital-twin development, the analysis indicates whether a digital representation captures the important behavior of its real-system counterpart. More broadly, it gives engineers evidence about the strengths and limitations of a model before they use its outputs to guide decisions. By exposing missed behavior and uncertainty, the process improves confidence in engineering conclusions and directs targeted refinement.