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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Types of Collisions - II01:19

Types of Collisions - II

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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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大型言語モデルによる複合衝突の断面予測

Zeyu Zhu1, Chengyi Xie2, Shaojie Lin1

  • 1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

Analytical chemistry
|September 1, 2025
PubMed
まとめ
この要約は機械生成です。

HyperCCSは,化学大言語モデル (CLLM) を使用したイオン移動質量スペクトロメトリーにおける化合物アノテーションの精度を高めます. この新しいフレームワークは,さまざまな分子に対する既存の方法を上回る衝突横断 (CCS) の予測を改善します.

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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関連する実験動画

Last Updated: Sep 9, 2025

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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科学分野:

  • コンピュータ化学
  • 分析化学
  • バイオインフォマティクス

背景:

  • 衝突横断 (CCS) は,イオン移動質量スペクトロメトリ (IM-MS) の正確な化合物識別に不可欠です.
  • 現在の計算型CCS予測方法は,限られたデータと不十分なマルチモダルの機能処理で,不適切なパフォーマンスをもたらします.
  • IM-MSアプリケーションのための大規模な化合物データベースの構築には,正確なCCS予測が不可欠です.

研究 の 目的:

  • 正確な衝突横断 (CCS) 予測のための新しい計算フレームワーク,HyperCCSを開発する.
  • 複雑な分子情報を捉えるための化学大言語モデル (CLLM) を活用する.
  • IM-MSの予測性能を改善するために,マルチモダルの機能を効果的に統合する.

主な方法:

  • 化学的な大きな言語モデル (CLLM) を微調整し,広範囲な SMILES 配列で事前に訓練した.
  • CLLMから派生した特徴を他の異質なデータと統合するためのクロスモダル機能融合モジュールを開発しました.
  • ベンチマークデータセット (METLIN-CCS,AllCCS2) と社内の実験データで評価されたハイパーCCS.

主要な成果:

  • ハイパーCCSは,さまざまな分子量,アダクトタイプ,イオンモードにおける堅実なCCS予測を実証し,既存の方法を上回った.
  • このフレームワークは,実験データ上の高質量アナリストに,同位体を正確に分解し,予測を推論した.
  • SHAP分析とアブレーション研究では,CLLMの特徴と融合メカニズムの有意な貢献が確認されました.

結論:

  • ハイパーCCSは,IM-MSの計算式CCS予測に重要な進歩をもたらします.
  • CLLMとクロスモダル融合の統合は,以前の予測モデルの限界を効果的に解決します.
  • HyperCCSは,メタボロミクスと構造生物学の研究のための高通量,適応可能な計算ツールを提供します.