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Related Concept Videos

Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

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Procedural Guide for Assessing Axillary Body Temperature using a Digital Thermometer:
Step 1: Perform hand hygiene and put on clean gloves to maintain infection control and prevent cross-contamination.
Step 2: Prepare the patient by explaining the procedure to ensure understanding and cooperation. Ensure privacy, expose the axilla, and inform the patient that minimal movement is crucial for an accurate reading.
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Thermal expansion and Thermal stress: Problem Solving01:27

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San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55...
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Equipments Used to Measure Body Temperature01:13

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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,...
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Assessing Body Temperature - Temporal Artery01:19

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Here is a stepwise guide to assessing the body temperature at the temporal artery using a temporal artery thermometer
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Step 3: Assess the patient's...
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Difference from Background: Limit of Detection01:05

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Thermosensation01:43

Thermosensation

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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...
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Infrared Thermography for the Detection of Changes in Brown Adipose Tissue Activity
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Perimeter Security Utilizing Thermal Object Detection.

Georgios Orfanidis1, Konstantinos Ioannidis1, Stefanos Vrochidis1

  • 1Centre for Research and Technology Hellas-CERTH, Thermi, GR 57001 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

Thermal imaging offers robust, 24-hour detection for security systems, outperforming traditional methods. This study evaluates artificial intelligence object detection models in the thermal spectrum for enhanced surveillance capabilities.

Keywords:
Infraredcritical infrastructureobject detectionsurveillancethermal

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Area of Science:

  • Computer Science
  • Engineering
  • Security Technology

Background:

  • Artificial intelligence (AI) is increasingly applied across diverse fields, including security and surveillance.
  • Automatic surveillance systems are becoming more prevalent, driving demand for advanced technologies.
  • Thermal detection offers advantages like 24-hour operation and robustness against environmental changes.

Purpose of the Study:

  • To evaluate the effectiveness of thermal automatic detection systems for surveillance.
  • To assess the role of AI object detection models in the thermal spectrum for security.
  • To determine the suitability of thermal imaging for reliable and efficient threat detection.

Main Methods:

  • Examining object detection models operating exclusively in the thermal/infrared spectrum.
  • Evaluating system performance based on detection capabilities and efficiency.
  • Assessing robustness against varying illumination and weather conditions.

Main Results:

  • Thermal detection provides continuous, uninterrupted surveillance capabilities.
  • AI models in the thermal spectrum show potential for efficient threat identification.
  • The technology demonstrates resilience to environmental and illumination challenges.

Conclusions:

  • Thermal automatic detection systems are a viable component for advanced surveillance.
  • AI-powered thermal imaging enhances the reliability and efficiency of security mechanisms.
  • Further evaluation of thermal object detection models is recommended for surveillance applications.