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Related Concept Videos

Glassware Calibration01:11

Glassware Calibration

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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.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
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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.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
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Steps in the Modeling Process01:14

Steps in the Modeling Process

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
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Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

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Root loci often diverge as system poles shift from the real axis to the complex plane. Key points in this transition are the breakaway and break-in points, indicating where the root locus leaves and reenters the real axis. The branches of the root locus form an angle of 180/n degrees with the real axis, where n is the number of branches at a breakaway or break-in point.
The maximum gain occurs at the breakaway points between open-loop poles on the real axis, while the minimum gain is...
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Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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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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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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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.
For data that follow a straight line, the standard method for fitting is the linear...
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Related Experiment Video

Updated: Jan 21, 2026

Simulation of the Planetary Interior Differentiation Processes in the Laboratory
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Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes.

Aditya Sai1, Carolina Vivas-Valencia1, Thomas F Imperiale2,3,4

  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|August 4, 2019
PubMed
Summary

This study uses Gaussian process regression (GPR) and multiobjective optimization to calibrate cancer models, finding that different goodness-of-fit criteria highlight distinct aspects of cancer natural history for improved prevention strategies.

Keywords:
Gaussian processPareto frontiercalibrationcancer simulationgoodness-of-fit criterionmicrosimulationregression

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Area of Science:

  • Computational biology
  • Cancer research
  • Statistical modeling

Background:

  • Model calibration is crucial for understanding cancer natural history and informing prevention strategies.
  • Complex cancer simulation models require efficient calibration methods, especially with multiple targets.
  • Pareto frontiers help identify optimal parameters but face computational challenges in high-dimensional spaces.

Purpose of the Study:

  • To explore multiobjective calibration using Gaussian process regression (GPR).
  • To investigate how multiple goodness-of-fit (GOF) criteria identify Pareto-optimal parameters.
  • To apply these methods to colorectal cancer (CRC) natural history modeling.

Main Methods:

  • Applied GPR metamodels to estimate CRC prevalence rates from the Colon Modeling Open Source Tool (CMOST).
  • Embedded GPR within a Pareto optimization framework using genetic algorithms.
  • Utilized both sum-of-squared errors (SSE) and Poisson deviance as GOF criteria.

Main Results:

  • GPR accurately approximated CMOST outputs for two parameter sets.
  • Different GOF criteria identified distinct Pareto-optimal parameter sets.
  • SSE prioritized age-specific parameters, while Poisson prioritized adenoma-specific parameters.

Conclusions:

  • Diverse GOF criteria reveal different facets of CRC natural history.
  • Combining multiobjective optimization, GPR, and varied GOF criteria enhances model calibration.
  • This approach identifies optimal parameter regions for cancer models.