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相关概念视频

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.
Step 3: Adjust the patient’s clothing to expose only the axilla. It minimizes...
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Quantifying Heat02:46

Quantifying Heat

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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a...
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Equipments Used to Measure Body Temperature01:13

Equipments Used to Measure Body Temperature

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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
Step 1: Perform hand hygiene and don a fresh pair of gloves to prevent cross-infection and ensure patient safety.
Step 2: Explain the procedure to the patient to establish trust. Clear communication establishes trust with the patient, ensures they understand what to expect, promotes cooperation, and enhances comfort during the procedure.  
Step 3: Assess the patient's...
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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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Assessing Body Temperature - Oral01:14

Assessing Body Temperature - Oral

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Here are the steps to accurately measure oral temperature using an electronic thermometer:
Step 1:
Start by practicing proper hand hygiene to prevent the spread of microorganisms.
Step 2:
Take the thermometer out of the charging unit, switch it on, and wait for the ready sign.
Step 3:
Gently slide the probe cover until a click is heard. This simple action prevents cross-contamination and ensures the correct placement of the probe cover.
Step 4:
Instruct the patient to open their mouth and place...
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Assessment of Sensory Thresholds in Dogs Using Mechanical and Hot Thermal Quantitative Sensory Testing
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通过机器学习估计军事工作犬核心温度变化:一个模拟研究.

Tanya M Tebcherani1, Phillip T Koshute2, Daniel C Hooper3

  • 1Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA; Johns Hopkins University Department of Mechanical Engineering, 3400 N Charles St, Baltimore, MD, 21218, USA.

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机器学习模型可以估计军事工作狗的核心温度变化,建立在基于物理的模型. K9-TempML模型对预测工作犬的高温症风险有希望.

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科学领域:

  • 兽医医学 兽医医学 兽医医学
  • 机器学习应用 机器学习应用
  • 动物生理学 动物生理学

背景情况:

  • 军事工作犬对美国军事任务至关重要.
  • 暴露在炎热的环境中会带来显著的高温症风险,这是这些狗的主要死亡原因.
  • 现有的基于物理的模型估计了核心温度的变化,但可能缺乏适应性.

研究的目的:

  • 探索使用机器学习 (ML) 模拟军事工作狗核心温度变化的可行性.
  • 通过推断现实世界的数据关系来补充基于物理的方法,开发一个ML模型.
  • 为了提高模型的可用性和适用于非典型温度模式的适用性.

主要方法:

  • 使用三种ML模型对基于物理的模型进行了近似计算.
  • 开发并分析了一个随机森林模型,称为K9-TempML.
  • 在K9-TempML模型上进行特征分析和增强研究.

主要成果:

  • 证明了将ML应用到模型犬核心温度变化的可行性.
  • K9-TempML模型提供了与基于物理模型最接近的核心温度变化估计.
  • 功能分析表明,删除难以收集的功能可以保持准确性,同时提高可用性.

结论:

  • 机器学习为模拟军事工作犬核心温度提供了一种可行且互补的方法.
  • K9-TempML模型显示了准确的温度变化估计和特征重要性分析的潜力.
  • 未来的工作将集中在现实世界的数据培训和将其整合到军事应用的实际工具中.