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Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Electronic Distance Measuring Instruments01:30

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Updated: Nov 22, 2025

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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A Cyber-Physical Platform for Model Calibration.

Lucia Bandiera1,2, David Gomez-Cabeza1, Eva Balsa-Canto3

  • 1School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh, UK.

Methods in Molecular Biology (Clifton, N.J.)
|January 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a cyber-physical platform for automated mathematical model calibration in synthetic biology, significantly reducing effort and accelerating field applications.

Keywords:
Mathematical modelingMicrofluidicsOptimal experimental designSynthetic biologySystem identification

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Area of Science:

  • Synthetic biology
  • Computational biology
  • Mathematical modeling

Background:

  • Mathematical models are crucial for synthetic biology but are often difficult and time-consuming to infer.
  • Traditional model inference methods present significant barriers to widespread adoption in synthetic biology.

Purpose of the Study:

  • To present a protocol for a cyber-physical platform that automates mathematical model calibration.
  • To enable synthetic biologists to accelerate the development of new applications through efficient modeling.

Main Methods:

  • Integration of in silico simulations with in vitro/vivo experiments.
  • Development of a cyber-physical platform for automated model calibration.
  • Providing a detailed protocol and accessible code for platform operation.

Main Results:

  • Demonstrated significant reduction in effort for model calibration (60-80%).
  • Successful integration of computational simulations and experimental data.
  • Creation of a reproducible protocol for synthetic biology modeling.

Conclusions:

  • The developed cyber-physical platform offers a practical solution to the challenges of mathematical model inference in synthetic biology.
  • This approach can accelerate the progression of synthetic biology toward real-world applications by streamlining the modeling process.