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関連する概念動画

Control Volume and System Representations01:16

Control Volume and System Representations

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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
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Physical Methods for Controlling Microbial Growth: Temperature01:23

Physical Methods for Controlling Microbial Growth: Temperature

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Heat is a widely used method to control microbial growth by targeting and denaturing cellular proteins, thereby killing or inactivating microbes. This method's effectiveness is quantified using parameters such as the thermal death point (TDP), thermal death time (TDT), and decimal reduction time (D value). TDP represents the lowest temperature at which all microorganisms in a liquid suspension are eliminated within 10 minutes, whereas TDT is the time necessary to achieve sterilization at a...
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Physical Methods for Controlling Microbial Growth: Radiation and Filtration01:26

Physical Methods for Controlling Microbial Growth: Radiation and Filtration

1.0K
Radiation and filtration are essential tools for microbial control, targeting microorganisms through distinct mechanisms. Radiation eliminates microbes by damaging their DNA, either killing them or inhibiting their growth. Based on wavelength, radiation is classified into two types: nonionizing and ionizing radiation.Non-ionizing radiation, such as UV radiation (200–400 nm), is absorbed by DNA, causing defects that effectively disinfect surfaces, air, and water, including safety cabinets.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
540
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

921
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
921
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

498
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
498

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Updated: Jan 23, 2026

High Resolution Physical Characterization of Single Metallic Nanoparticles
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MolCluster：物理的に一貫した解像度制御を備えたマルチスケール分子表現のための教師なしフレームワーク

Zhixuan Zhong1,2, Linbo Ma3, Jian Jiang1,2

  • 1Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Polymer Physics and Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, P. R. China.

Journal of chemical theory and computation
|January 21, 2026
PubMed
まとめ
この要約は機械生成です。

MolClusterは、教師なしモデルであり、グラフニューラルネットワークを使用して正確な粗視化（CG）分子モデルを作成します。この新しいアプローチは、従来のメソッドの制限を克服し、複雑なシステムでカスタマイズ可能な解像度を可能にします。

キーワード:
粗視化モデリンググラフニューラルネットワーク教師なし学習分子動力学マルチスケールモデリング

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Last Updated: Jan 23, 2026

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科学分野:

  • 計算化学
  • 材料科学
  • 生物物理学

背景:

  • 従来の粗視化（CG）モデリングは、多様な化学構造と固定マッピング規則に課題があります。
  • 教師ありCGメソッドは、小さなラベル付きデータセットとマルチスケールモデリングの解像度制御の欠如によって制限されます。

研究 の 目的:

  • カスタマイズ可能な粗視化表現を抽出するための教師なしモデルMolClusterを開発すること。
  • 多様な分子システムのマッピング解像度を正確に制御できるようにすること。

主な方法:

  • グラフニューラルネットワークとコミュニティ検出の統合により、教師なしCG表現抽出を実現。
  • ターゲットグループの保存のための事前定義されたグループペア損失の実装。
  • カスタマイズ可能な解像度制御のための二分法戦略の利用。

主要な成果:

  • MolClusterは、MARTINI2データセットにおいて、従来のモデルおよび教師ありモデルと比較して、CGマッピングおよびビーズタイプ予測において優れたパフォーマンスを示しました。
  • ラベルフリーの事前トレーニング戦略は、MolClusterの効果を大幅に向上させます。

結論:

  • MolClusterは、化学的に一貫性があり、カスタマイズ可能なCGマッピングのための新しい教師なしアプローチを提供します。
  • このモデルは、ポリマー、タンパク質、および複雑なマルチスケールシステムへの応用において大きな可能性を示しています。