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相关概念视频

Molar Mass01:54

Molar Mass

86.1K
The identity of a substance is defined not only by the types of atoms or ions it contains but by the quantity of each type of atom or ion. For example, water, H2O, and hydrogen peroxide, H2O2, are alike in that their respective molecules are composed of hydrogen and oxygen atoms. However, because a hydrogen peroxide molecule contains two oxygen atoms, as opposed to the water molecule, which has only one, the two substances exhibit very different properties.
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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

753
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.
In this model, each generator is connected to a...
753
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

562
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.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
562
Applications of the Ideal Gas Law: Molar Mass, Density, and Volume03:43

Applications of the Ideal Gas Law: Molar Mass, Density, and Volume

63.2K
The volume occupied by one mole of a substance is its molar volume. The ideal gas law, PV = nRT,  suggests that the volume of a given quantity of gas and the number of moles in a given volume of gas vary with changes in pressure and temperature. At standard temperature and pressure, or STP (273.15 K and 1 atm), one mole of an ideal gas (regardless of its identity) has a volume of about 22.4 L — this is referred to as the standard molar volume.
63.2K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.4K
VSEPR Theory for Determination of Electron Pair Geometries
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Machines01:19

Machines

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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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相关实验视频

Updated: Jan 21, 2026

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model
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一个基于机器学习的预测模型,用于下第三提取困难:结合多式特征和SHAP分析.

Piaopiao Qiu1, Jiaqi Huang1, Huasheng Zhang1

  • 1Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Oral and Maxillofacial Surgery, Shanghai Tongji Stomatological Hospital and Dental School, Tongji University, 399 Yanchang Middle Road, Jing'an District, Shanghai, Asia, 200092, China.

BMC oral health
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概括
此摘要是机器生成的。

机器学习模型使用圆束计算断层扫描 (CBCT) 数据准确预测下三提取难度. 形态特征,如牙角,是关键预测因素,超过了初级临床医生.

关键词:
牙第三骨提取难度 手术前评估 机器学习 多式模式参数

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科学领域:

  • 口腔和牙面部外科手术
  • 牙科成像 牙科成像 牙科成像
  • 机器学习在医学中的应用

背景情况:

  • 预测下第三 (MM3) 提取难度对于手术规划至关重要.
  • 当前的方法通常依赖于主观的临床判断,导致变化.

研究的目的:

  • 为MM3提取难度开发一个快速而准确的预测模型.
  • 将机器学习与多模式参数集成,包括CBCT成像.

主要方法:

  • 创建了一个数据集,结合了临床数据和自动化的CBCT形态特征.
  • 训练和优化了六个机器学习模型 (SVM,ANN,XGBoost,RF,KNN,物流回归).
  • 用SHAP和RFE分析来确定特征的重要性和模型验证.

主要成果:

  • XGBoost模型实现了最高的预测准确度 (88.24%),超过了初级临床医生 (83.53%).
  • 形态特征,特别是邻近的牙角,接触面积和MM3体积是主要的预测因素.
  • 纤维素素和前列血时间等临床因素也为预测做出了贡献.

结论:

  • 整合形态和临床特征显著提高了MM3提取难度的预测准确性.
  • 邻近的牙阻力成为最有影响力的因素,其次是骨阻力和下管道附近.