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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.
86.1K
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.
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Machines01:19

Machines

559
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...
559

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関連する実験動画

Updated: Jan 21, 2026

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model
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下顎智歯抜歯難易度予測のための機械学習ベースの予測モデル：マルチモーダル特徴量とSHAP分析の組み込み

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
|January 19, 2026
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まとめ

機械学習モデルは、コーンビームCT（CBCT）データを使用して下顎智歯抜歯難易度を正確に予測する。歯の角度などの形態学的特徴量が主要な予測因子であり、若手臨床医を上回った。

キーワード:
下顎智歯抜歯抜歯難易度術前評価機械学習マルチモーダルパラメータ

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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科学分野:

  • 口腔外科
  • 歯科画像診断
  • 医療における機械学習

背景:

  • 下顎智歯（MM3）抜歯の難易度予測は、手術計画において非常に重要です。
  • 現在の方法は主観的な臨床判断に依存することが多く、ばらつきが生じます。

研究 の 目的:

  • MM3抜歯難易度の迅速かつ正確な予測モデルを開発すること。
  • 機械学習とCBCT画像を含むマルチモーダルパラメータを統合すること。

主な方法:

  • 臨床データと自動化されたCBCT形態学的特徴量を組み合わせたデータセットを作成しました。
  • 6つの機械学習モデル（SVM、ANN、XGBoost、RF、KNN、ロジスティック回帰）を訓練および最適化しました。
  • 特徴量の重要性とモデル検証のためにSHAPおよびRFE分析を使用しました。

主要な成果:

  • XGBoostモデルは、若手臨床医（83.53%）を上回る最高の予測精度（88.24%）を達成しました。
  • 形態学的特徴量、特に隣接歯の角度、接触面積、MM3容積が主要な予測因子でした。
  • フィブリノーゲンやプロトロンビン時間などの臨床的因子も予測に寄与しました。

結論:

  • 形態学的特徴量と臨床的特徴量を統合することにより、MM3抜歯難易度の予測精度が大幅に向上します。
  • 隣接歯の抵抗が最も影響力のある要因であり、次いで骨抵抗と下顎管への近接性が続きました。