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関連する概念動画

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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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Members Made of Elastoplastic Material01:19

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The behavior of elastoplastic materials under bending stresses, particularly in structural members with rectangular cross-sections, is crucial for predicting material responses and understanding failure modes. Initially, when a bending moment is applied, the stress distribution across the section follows Hooke's Law and is linear and elastic. This distribution means the stress increases from the neutral axis to the maximum at the outer fibers, up to the elastic limit.
As the bending moment...
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Within the human body, a complex and detailed system of trillions of cells works in unison to sustain life. Each cell houses a nucleus, which contains 46 chromosomes divided into 23 pairs. Chromosomes are highly coiled structures made of the genetic material DNA. These chromosomes are essential carriers of genetic information, with half inherited from the mother through her egg and the other half from the father's sperm, combining to create the unique genetic makeup of an individual.
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機械学習による超非圧縮性超硬質材料の検索

Aria Mansouri Tehrani1, Anton O Oliynyk1, Marcus Parry2

  • 1Department of Chemistry , University of Houston , Houston , Texas 77204 , United States.

Journal of the American Chemical Society
|July 17, 2018
PubMed
まとめ

機械学習により 新しい超硬質物質の発見が加速されます 研究者らは,レニウム・ボルンガム炭化物とモリブデン・ボルンガムボルンガム炭化物を合成し,その硬さは40GPaを超える.

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科学分野:

  • 材料科学
  • コンピュータ材料科学
  • 固体化学

背景:

  • 特殊な機械的特性,特に高い硬さを持つ材料の開発は大きな課題です.
  • 予測モデリングは 新しく機能する無機材料の発見を加速させることができます

研究 の 目的:

  • 材料の硬さのプロキシとして弾性モジュールを予測する機械学習モデルを開発する.
  • 超非圧縮性および超硬質の新種の無機化合物を特定し合成する.

主な方法:

  • 水晶構造データベースから118,287の化合物をスクリーニングするために,サポートベクトルマシン回帰モデルが使用されました.
  • テルナリウム・ボルンガム・カーバイドとクォーターナリウム・ボルンガム・ボルンガム・カーバイドは,環境圧で合成された.
  • 高圧ダイヤモンド・アンビル・セル測定とビッカース微硬性試験が行われた.

主要な成果:

  • 機械学習は10%未満の誤差で 合成化合物の質量モジュールを正確に予測しました
  • 両合成化合物は超不圧縮性であり,非常に高い硬さ (> 40 GPa) を示した.
  • 特定された材料は,低インデント負荷で超硬度値を超えました.

結論:

  • 機械学習は 先進的な機能的な無機材料の発見を加速させるための 効果的な戦略です
  • 開発されたモデルは,例外的な機械的性質を持つ新しい超硬質化合物を成功裏に特定しました.
  • この研究は,対象となる材料の設計と合成のための強力なアプローチを示しています.