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Band Theory02:35

Band Theory

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When two or more atoms come together to form a molecule, their atomic orbitals combine and molecular orbitals of distinct energies result. In a solid, there are a large number of atoms, and therefore a large number of atomic orbitals that may be combined into molecular orbitals. These groups of molecular orbitals are so closely placed together to form continuous regions of energies, known as the bands.
The energy difference between these bands is known as the band gap.
Conductor, Semiconductor,...
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Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
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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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Energy Bands in Solids01:01

Energy Bands in Solids

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Isolated atoms have discrete energy levels that are well described by the Bohr model. And, it quantifies the energy of an electron in a hydrogen atom as En. Higher quantum numbers 'n' yield less negative, closer electron energy levels.
 Band Formation:
When atoms are brought close together, as in a solid, these discrete energy levels begin to split due to the overlap of electron orbitals from adjacent atoms. This split occurs because of the Pauli exclusion principle, which states...
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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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Updated: Jan 24, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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機械学習駆動型ADHD分類:VMDサブバンド解析による投薬効果の検討

Ebru Aker1, Şerife Gengeç Benli2, Zeynep Ak1

  • 1Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Erciyes University, Kayseri, Turkey.

Current computer-aided drug design
|January 23, 2026
PubMed
まとめ

本研究では、安静時fMRIデータに対する変動モード分解(VMD)を用いて、注意欠陥・多動性障害(ADHD)のサブタイプを正確に分類し、投薬効果を評価することで、客観的な診断ツールを提供する。

キーワード:
ADHD分類ADHDサブタイプfMRI信号分解機械学習投薬サブバンド

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科学分野:

  • 神経画像
  • 計算神経科学
  • 医療情報学

背景:

  • 注意欠陥・多動性障害(ADHD)は一般的な神経発達障害である。
  • 現在のADHD診断は主観的な評価に依存しており、客観的かつデータ駆動型の手法が必要とされている。
  • 神経画像、特に安静時fMRIは、客観的なADHD評価の可能性を提供する。

研究 の 目的:

  • 安静時fMRIデータを用いたADHDサブタイプの分類。
  • ADHD分類における投薬の影響の評価。
  • ADHDの客観的なコンピュータ支援診断アプローチの開発。

主な方法:

  • ADHD-200データセットからの安静時fMRIデータを解析した。
  • 機能的MRI信号を1Dに変換し、変動モード分解(VMD)を用いてサブバンドに分解した。
  • 統計的特徴量を抽出し、サポートベクターマシン(SVM)、線形判別分析(LDA)、人工ニューラルネットワーク(ANN)を用いて分類した。

主要な成果:

  • VMD由来の特徴量は分類性能を著しく向上させた。
  • LDAは高い精度を達成した:非投薬ADHD対対照群で96.34%、投薬ADHD対対照群で88.41%。
  • 投薬ADHD対非投薬ADHDの分類精度は79.63%、全群にわたる3値分類は69.51%であった。

結論:

  • VMDベースのアプローチは、ADHDサブタイプの分類と投薬効果の評価を効果的に改善する。
  • この方法は、ADHDの診断と治療計画のための客観的なツールとして有望である。
  • ADHD神経画像データの複雑さは、多クラス分類精度の課題となる。