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Temperature Measurement Sites01:14

Temperature Measurement Sites

2.2K
A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
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Equipments Used to Measure Body Temperature01:13

Equipments Used to Measure Body Temperature

1.2K
Body temperature can be assessed using various devices and measured in Celsius or Fahrenheit.
Glass-bulb Thermometer:
Glass-bulb thermometers are hollow glass tubes with a bulb tip containing liquid such as ethanol or mercury. Historically, glass bulb mercury thermometers were the standard device to measure body temperature. Today, mercury thermometers are prohibited in many countries due to the hazardous effects of mercury and the risk of exposure if the glass bulb breaks. In general,...
1.2K
Thermosensation01:43

Thermosensation

31.8K
Peripheral thermosensation is the perception of external temperature. A change in temperature (on the surface of the skin and other tissues) is detected by a family of temperature-sensitive ion channels called Transient Receptor Potential, or TRP, receptors. These receptors are located on free nerve endings. Those detecting cold temperatures are closer to the surface of the skin than the nerve endings detecting warmth. These thermoTRP channels, while temperature selective, have relatively...
31.8K

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Related Experiment Video

Updated: Sep 16, 2025

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors
08:49

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

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Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors.

Jiri Konecny1, Jaromir Konecny1, Kamil Bancik1

  • 1Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study predicts soil temperature profiles for powering Internet of Things (IoT) sensors using machine learning. The enhanced model achieves lower error and simplifies inputs for efficient energy harvesting.

Keywords:
Internet-of-Things sensorsenergy harvestinglong short-term memorypolynomial regressionsupport vector regressiontemperature modelling

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Area of Science:

  • Environmental Science
  • Agricultural Technology
  • Sensor Networks

Background:

  • Powering Internet of Things (IoT) sensors for environmental and agricultural applications is a significant challenge.
  • Exploiting temperature differences between air and soil presents a promising solution for energy harvesting.
  • Accurate soil temperature profile data is crucial for effective energy-harvesting technologies.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting soil temperature profiles.
  • To optimize energy harvesting for IoT sensors by improving soil temperature prediction accuracy and efficiency.
  • To simplify the input parameters for soil temperature prediction models.

Main Methods:

  • Utilized meteorological and soil temperature profile data from the Czech Republic.
  • Trained machine learning models including Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM).
  • Simplified model inputs to ambient temperature and solar irradiance.

Main Results:

  • Achieved a prediction error of 0.79 °C for soil temperature profiles.
  • Demonstrated a 10.9% reduction in temperature error compared to state-of-the-art studies.
  • Significantly reduced computational costs by simplifying input parameters.

Conclusions:

  • The proposed machine learning approach provides a more efficient and accurate method for predicting soil temperature.
  • This advancement facilitates optimized energy harvesting for IoT sensors in environmental and agricultural settings.
  • Simplified input parameters enhance the practicality and cost-effectiveness of the solution.