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Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are...
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Related Experiment Video

Updated: Jan 27, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Bayesian network modelling of hierarchical composite indicators.

David Requejo-Castro1, Ricard Giné-Garriga2, Agustí Pérez-Foguet1

  • 1Engineering Sciences and Global Development (EScGD), Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya · BarcelonaTech (UPC), Jordi Girona, 1-3, 08034 Barcelona, Spain.

The Science of the Total Environment
|March 15, 2019
PubMed
Summary
This summary is machine-generated.

Bayesian networks (BNs) effectively replicate composite indicator (CI) frameworks for water, sanitation, and hygiene (WaSH) monitoring. This approach quantifies key components and their interrelationships, enhancing data analysis and decision-making in the WaSH sector.

Keywords:
Bayesian networksComposite indicators (CIs)InterlinkagesSystematic methodologyWater, sanitation and hygiene (WaSH)

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Area of Science:

  • Environmental Science
  • Public Health
  • Data Science

Background:

  • The water, sanitation, and hygiene (WaSH) sector utilizes various multidimensional monitoring tools, including composite indicator (CI)-based frameworks.
  • Existing CI frameworks often overlook the interrelationships between the indicators they comprise.
  • Bayesian networks (BNs) are emerging as powerful tools for WaSH assessment and decision support.

Purpose of the Study:

  • To evaluate the validity, reliability, and feasibility of using Bayesian networks (BNs) to replicate existing CI-based conceptual frameworks in the WaSH sector.
  • To propose a semi-automatic, data-driven methodology for applying BNs to WaSH monitoring.
  • To assess the performance of BNs using data from the Rural Water Supply and Sanitation Information System (SIASAR) initiative.

Main Methods:

  • A data-driven, semi-automatic methodology was developed to apply Bayesian networks (BNs).
  • The methodology involved calibrating and validating the BN model using data from the SIASAR initiative across two countries.
  • The study compared BN performance with existing CI-based frameworks, focusing on model inference capacity and component identification.

Main Results:

  • BN model inference capacity significantly improved when network structures were informed by CI-based frameworks.
  • BNs successfully reduced and quantified key components influencing objective variables, offering advantages for data updating.
  • The analysis identified interlinkages among WaSH components, suggesting potential for enhanced interdisciplinary collaboration.

Conclusions:

  • Bayesian networks (BNs) accurately replicate CI-based conceptual frameworks in the WaSH sector.
  • BNs offer a robust method for quantifying indicator interrelationships and improving data analysis for WaSH monitoring.
  • The study highlights the significant potential of BNs for broader application in WaSH planning and decision-making.