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関連する概念動画

Metallic Solids02:37

Metallic Solids

20.5K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
20.5K
Structures of Solids02:22

Structures of Solids

17.6K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
17.6K
Conditions on Early Earth02:06

Conditions on Early Earth

101.0K
Around 4 billion years ago, oceans began to condense on earth while volcanic eruptions released nitrogen, carbon dioxide, methane, ammonia, and hydrogen into the primordial atmosphere. However, organisms with the characteristics of life were not initially present on earth. Scientists have used experimentation to determine how organisms evolved that could grow, reproduce, and maintain an internal environment.
101.0K
Machines01:19

Machines

564
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...
564
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.0K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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固体地球科学におけるデータ主導の発見のための機械学習

Karianne J Bergen1,2, Paul A Johnson3, Maarten V de Hoop4

  • 1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.

Science (New York, N.Y.)
|March 23, 2019
PubMed
まとめ
この要約は機械生成です。

固体地球の地質科学は,複雑な地下プロセスを理解する上で課題に直面しています. 機械学習は,増加したデータを分析し,地球科学の発見のためのコンピュータシミュレーションを改善することによって,進歩を加速させる有望なアプローチを提供します.

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科学分野:

  • 固体地球地球科学
  • 地理学
  • 地力学

背景:

  • 地球の行動を理解することは 極めて重要ですが 複雑で多面的なプロセスによって 妨げられています
  • 地球の地下を直接観測することは 極めて限られている.
  • データの可用性やコンピュータによるシミュレーションの進歩は 研究の新たな道を開きます

研究 の 目的:

  • 固体地球科学における機械学習アプリケーションの現状をレビューする.
  • 科学的発見を加速させるための課題と機会を特定する.
  • この分野を拡大し,進めるための勧告を提供すること.

主な方法:

  • 地球科学における機械学習に関する既存の文献とケーススタディのレビュー.
  • データベースのアプローチと高度なシミュレーションの可能性を分析する.
  • 現在の能力と将来の研究方向の統合

主要な成果:

  • 機械学習 (ML) は,地球科学の進歩において重要な役割を果たす準備ができています.
  • より多くのデータと洗練されたシミュレーションを MLと組み合わせれば 地球の複雑なシステムの理解を深めることができます
  • MLを統合するには,データ処理とモデルの解釈性に関する特定の課題に取り組む必要があります.

結論:

  • 機械学習は,固体地球科学における固有の複雑性と観測上の限界を克服するために不可欠です.
  • 進歩を加速するために,ML技術のさらなる開発と適用が推奨されます.
  • 分野間の協力と戦略的投資は 地球科学における機械学習の潜在能力を 十分に発揮するための鍵となります