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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Golem: an algorithm for robust experiment and process optimization.

Matteo Aldeghi1,2,3, Florian Häse1,2,3,4, Riley J Hickman2,3

  • 1Vector Institute for Artificial Intelligence Toronto ON Canada matteo.aldeghi@vectorinstitute.ai alan@aspuru.com.

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|November 25, 2021
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Summary
This summary is machine-generated.

Golem is a new algorithm for robust optimization in science and engineering. It ensures reproducible results by identifying solutions resilient to experimental variability and process uncertainty.

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Area of Science:

  • Scientific and engineering optimization
  • Autonomous experimental platforms
  • Robust experimental design

Background:

  • Optimization tasks are common in science and engineering, often using design of experiments and algorithms.
  • Current strategies typically assume exact and reproducible conditions, neglecting variability.
  • This assumption leads to suboptimal performance and irreproducible results when conditions vary.

Purpose of the Study:

  • Introduce Golem, an algorithm for robust experiment and process optimization.
  • Enable optimization strategies to account for input uncertainty and variability.
  • Ensure reproducible performance of optimized experimental protocols.

Main Methods:

  • Golem is an algorithm agnostic to specific experiment planning strategies.
  • It identifies optimal solutions that are robust against input uncertainty.
  • Can analyze past experiment robustness or guide planning algorithms in real-time.

Main Results:

  • Golem ensures reproducible performance of optimized experimental protocols and processes.
  • Demonstrated through extensive benchmark studies and optimization of an analytical chemistry protocol.
  • Successfully optimized a protocol despite significant noise in experimental conditions.

Conclusions:

  • Golem provides a method for robust optimization in scientific and engineering applications.
  • It enhances the reliability and reproducibility of experimental outcomes.
  • Applicable to diverse fields requiring optimization under uncertain conditions.