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相关概念视频

Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
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Simplified Synchronous Machine Model01:30

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Classifying Matter by State02:49

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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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The Thyroid Gland

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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可解释机器学习模型使用数字化美国特征来分类复杂的甲状腺结节

Zhuyao Li1, Yu Yan2, Xiang Li1

  • 1Department of Surgery, The First Affiliated Hospital of Zhengzhou University, No. 1 East Jianshe Road, Zhengzhou 450000, China.

Radiology. Artificial intelligence
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概括
此摘要是机器生成的。

一个新的可解释的机器学习模型UltraMC,使用数字化超声波特征准确地分类传统和复杂的木乃伊甲状腺结节. 这种白盒框架提高了甲状腺结节分类的诊断准确性.

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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 甲状腺结节很常见,需要准确的分类来进行适当的管理.
  • 区分良性和恶性甲状腺结节,特别是复杂的病例,仍然是一个临床挑战.
  • 当前的诊断方法可以从先进的计算方法中获益,以提高准确性.

研究的目的:

  • 开发一个数字化,可解释的机器学习分类模型,用于甲状腺结节.
  • 准确地识别复杂的甲状腺结节,并有效地诊断传统的甲状腺结节.
  • 将数字化超声波功能集成到白盒框架中,以加强分类.

主要方法:

  • 来自七个中国医疗中心 (2011-2021) 的甲状腺超声波图像的回顾性收集.
  • 开发UltraMC,这是一个两层可解释的分类模型,具有前端和后端网络.
  • 使用精度,灵敏度,特异性和ROC曲线对UltraMC的评估.

主要成果:

  • 数据集包括73826名患者;前端网络在常规结节方面实现了92.9%的准确性.
  • 后端网络为木乃伊甲状腺结节 (MTNs) 实现了88.5%的准确性.
  • 对于MTN分类,UltraMC的整体诊断准确率为91.8%,AUC值很高.

结论:

  • 两层可解释分类模型 (UltraMC) 显示了传统和木乃伊甲状腺结节的高诊断准确性.
  • 在白盒框架内的数字化超声波功能有效支持复杂的甲状腺结节的分类.
  • 这种方法为提高甲状腺结节评估的诊断能力提供了一个有希望的工具.