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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

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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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Video Experimental Relacionado

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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Clasificación del TDAH impulsada por aprendizaje automático: Exploración de los efectos de la medicación con análisis

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
Resumen

Este estudio utiliza la Descomposición de Modos Variacionales (VMD) en datos de fMRI en estado de reposo para clasificar con precisión los subtipos del Trastorno por Déficit de Atención e Hiperactividad (TDAH) y evaluar los efectos de la medicación, ofreciendo una herramienta de diagnóstico objetiva.

Palabras clave:
clasificación TDAHsubtipos TDAHdescomposición de señales fMRIaprendizaje automáticouso de medicaciónsubbandas

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Área de la Ciencia:

  • Neuroimagen
  • Neurociencia Computacional
  • Informática Médica

Sus antecedentes:

  • El Trastorno por Déficit de Atención e Hiperactividad (TDAH) es un trastorno del neurodesarrollo común.
  • El diagnóstico actual del TDAH se basa en evaluaciones subjetivas, lo que requiere métodos objetivos basados en datos.
  • La neuroimagen, en particular la fMRI en estado de reposo, ofrece potencial para la evaluación objetiva del TDAH.

Objetivo del estudio:

  • Clasificar subtipos de TDAH utilizando datos de fMRI en estado de reposo.
  • Evaluar los efectos de la medicación en la clasificación del TDAH.
  • Desarrollar un enfoque diagnóstico objetivo asistido por computadora para el TDAH.

Principales métodos:

  • Se analizaron datos de fMRI en estado de reposo del conjunto de datos ADHD-200.
  • Las señales de fMRI se convirtieron a 1D y se descompusieron en subbandas utilizando la Descomposición de Modos Variacionales (VMD).
  • Se extrajeron características estadísticas y se clasificaron utilizando Máquinas de Vectores de Soporte (SVM), Análisis Discriminante Lineal (LDA) y Redes Neuronales Artificiales (ANN).

Principales resultados:

  • Las características derivadas de VMD mejoraron significativamente el rendimiento de la clasificación.
  • LDA logró una alta precisión: 96,34 % (TDAH no medicado frente a controles) y 88,41 % (TDAH medicado frente a controles).
  • La precisión de la clasificación para TDAH medicado frente a no medicado fue del 79,63 %, y del 69,51 % para la clasificación ternaria en todos los grupos.

Conclusiones:

  • El enfoque basado en VMD mejora eficazmente la clasificación de subtipos de TDAH y la evaluación de los efectos de la medicación.
  • Este método muestra ser prometedor como herramienta objetiva para el diagnóstico del TDAH y la planificación del tratamiento.
  • La complejidad de los datos de neuroimagen del TDAH presenta desafíos para la precisión de la clasificación multiclase.