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Using a pH Meter09:28

Using a pH Meter

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Source: Laboratory of Dr. Zhongqi He - United States Department of Agriculture
Acids and bases are substances capable of donating protons (H+) and hydroxide ions (OH-), respectively. They are two extremes that describe chemicals. Mixing acids and bases can cancel out or neutralize their extreme effects. A substance that is neither acidic nor basic is neutral. The values of proton concentration ([H+]) for most solutions are inconveniently small and difficult to compare so that a more practical...
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Presented here is a protocol for collecting and processing underwater photogrammetry data, including a significantly simplified and fully automated image processing pipeline resulting in georeferenced and time-series aligned outputs ready for ecological data extraction, analysis, and application.
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Efficiency of Liquid-liquid Extraction09:21

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Liquid-liquid extraction (LLE) is a separation technique used instead of distillation when either: (a) the relative volatilities of the compounds to be separated are very similar; (b) one or more of the mixture components are temperature sensitive even near ambient conditions; (c) the distillation would require a very low pressure or a very high distillate/feed...
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Cryo-electron microscopy (cryoEM) multi-grid screening is often a tedious process that demands hours of attention. This protocol shows how to set up a standard Leginon collection and Smart Leginon Autoscreen to automate this process. This protocol can be applied to the majority of cryoEM holey foil...
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Measuring the yaw torque of tethered Drosophila with the torque meter allows the neuroscientist exquisite control of the stimulus situation of the experimental animal. Together with the unique genetic tools available in the fruit fly, this paradigm is used for a wide variety of neurobiological...
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Related Experiment Video

Updated: Jan 19, 2026

Using a pH Meter: Principles, Calibration and pH Measurement
09:28

Using a pH Meter: Principles, Calibration and pH Measurement

Published on: April 30, 2023

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An efficient algorithm for extracting appliance-time association using smart meter data.

Sarah Osama1, Marco Alfonse1, Abdel-Badeeh M Salem1

  • 1Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Heliyon
|September 10, 2019
PubMed
Summary
This summary is machine-generated.

This study extracts appliance usage patterns from smart meter data to build trust in energy management solutions. It reveals appliance-time associations, prioritizing user preferences for better Demand Response (DR) program adoption.

Keywords:
Appliance-time associationComputer scienceInternet of things (IoT)Smart meter

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Last Updated: Jan 19, 2026

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Area of Science:

  • Energy Management
  • Artificial Intelligence
  • Consumer Behavior

Background:

  • Demand Response (DR) programs are crucial for energy management but face challenges in gaining resident trust and respecting appliance usage preferences.
  • Extracting resident behavior from massive smart meter data is complex.
  • Understanding appliance-level energy consumption patterns is key to improving DR program effectiveness.

Purpose of the Study:

  • To extract temporal association patterns of energy consumption at the appliance level.
  • To enhance the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm for discovering appliance usage preferences over time.
  • To integrate appliance usage priority as a utility factor within temporal association rule mining.

Main Methods:

  • Extension of the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm.
  • Analysis of temporal association patterns in energy consumption data.
  • Incorporation of appliance usage priority and temporal partitioning (24-hour day) into the mining process.

Main Results:

  • Successful extraction of appliance-time association patterns from smart meter data.
  • Demonstration of appliance usage preferences at specific times.
  • Validation of the approach in considering appliance priority and temporal factors.

Conclusions:

  • The proposed method effectively extracts temporal appliance usage preferences, crucial for user-centric energy management.
  • Considering appliance priority enhances the relevance of discovered patterns for Demand Response programs.
  • This approach can significantly improve resident trust and participation in DR initiatives.