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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
Quality of Water01:19

Quality of Water

In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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.
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

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Related Experiment Video

Updated: Jun 23, 2026

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

Calibrating and validating bacterial water quality models: a Bayesian approach.

Andrew D Gronewold1, Song S Qian, Robert L Wolpert

  • 1Nicholas School of the Environment, Department of Statistical Science, Box 90328, Duke University, Durham, NC 27708-0328, USA. adg@duke.edu

Water Research
|April 28, 2009
PubMed
Summary
This summary is machine-generated.

Different methods for modeling fecal indicator bacteria (FIB) variability impact water quality predictions. Models without decay parameters may better represent FIB fate and transport, improving water resource management decisions.

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Last Updated: Jun 23, 2026

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

Area of Science:

  • Environmental Science
  • Water Quality Modeling
  • Microbiology

Background:

  • Water resource management relies on models to predict water quality under various pollutant loads.
  • Decisions on water use can be influenced by how models handle uncertainty and variability.
  • Few tools exist to incorporate fecal indicator bacteria (FIB) analysis uncertainty into predictive models.

Purpose of the Study:

  • To compare three methods for modeling variability in two FIB water quality models.
  • To evaluate how different uncertainty approaches affect model parameter estimates and predictive performance.
  • To propose an approach for assessing predictive performance using cross-validation and Bayesian posterior predictive p-values.

Main Methods:

  • Calibration of a first-order bacterial decay model using Ordinary Least Squares (OLS) and Bayesian Markov Chain Monte Carlo (MCMC).
  • Calibration of an empirical bacterial die-off model using OLS and MCMC.
  • Leave-one-out cross-validation to evaluate predictive performance and analyze Bayesian posterior predictive p-values.

Main Results:

  • Acknowledging uncertainty differently led to discrepancies in parameter estimates and predictive performance for FIB models.
  • Model calibration approaches influenced mean and variance estimates.
  • Bayesian posterior predictive p-values varied based on the modeling approach.

Conclusions:

  • Different methods for handling uncertainty in fecal indicator bacteria (FIB) modeling can yield varying results.
  • Models lacking a specific bacterial decay parameter may offer a more accurate representation of FIB fate and transport.
  • Improved understanding of uncertainty is crucial for reliable water quality predictions and effective water resource management.