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A reference frame accelerating or decelerating relative to an inertial frame is a non-inertial frame. To help understand this, consider what taking off in an airplane, turning a corner in a car, riding a merry-go-round, and the circular motion of a tropical cyclone all have in common. All these systems are accelerating, decelerating, or rotating relative to the Earth; hence, they all are non-inertial frames. All these systems exhibit inertial forces, which merely seem to arise from motion,...
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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Video Experimental Relacionado

Updated: Jan 22, 2026

A Modified EPA Method 1623 that Uses Tangential Flow Hollow-fiber Ultrafiltration and Heat Dissociation Steps to Detect Waterborne Cryptosporidium and Giardia spp.
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Evaluación de la calidad de imagen basada en aprendizaje profundo sin referencia para Cryptosporidium spp. y Giardia

Muhammad Amirul Aiman Asri1, Heshalini Rajagopal2, Norrima Mokhtar1

  • 1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, Kuala Lumpur, Malaysia.

PloS one
|January 20, 2026
PubMed
Resumen

Un nuevo modelo de aprendizaje profundo, PRIQA, evalúa la calidad de las imágenes de parásitos sin referencias. Supera a los métodos existentes, garantizando un análisis microscópico fiable para la salud pública.

Palabras clave:
aprendizaje profundoevaluación de la calidad de imagenparásitossalud públicadiagnósticomicroscopíainteligencia artificial

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Área de la Ciencia:

  • Parasitología; Imagen Médica; Ciencias de la Computación

Sus antecedentes:

  • La evaluación de la calidad de imagen (IQA) es crucial para la precisión diagnóstica.
  • Los modelos de IQA sin referencia (NR-IQA) carecen de enfoque en conjuntos de datos de microscopía, especialmente para parásitos como Cryptosporidium y Giardia.
  • Las características de alta calidad son esenciales para el aprendizaje automático en la detección de organismos parásitos.

Objetivo del estudio:

  • Desarrollar un novedoso modelo NR-IQA basado en aprendizaje profundo para imágenes de microscopía de parásitos.
  • Abordar la brecha en NR-IQA para conjuntos de datos de organismos parásitos microscópicos.
  • Mejorar la fiabilidad de los sistemas de inspección automatizada para la salud pública.

Principales métodos:

  • Se desarrolló PRIQA (Parasite ResNet-101 IQA), un modelo NR-IQA basado en aprendizaje profundo.
  • Se compararon nueve arquitecturas de Redes Neuronales Convolucionales Profundas (DCNN) utilizando Puntuaciones de Opinión Media (MOS) humanas.
  • Se utilizó ResNet-101 como extractor de características, mapeando características a MOS mediante regresión, y se comparó con diez algoritmos NR-IQA de última generación.

Principales resultados:

  • ResNet-101 fue identificado como el extractor de características más robusto para imágenes de parásitos.
  • PRIQA demostró un rendimiento superior en comparación con los métodos NR-IQA existentes.
  • El modelo identifica eficazmente imágenes de microscopía de parásitos poco fiables o de baja calidad.

Conclusiones:

  • PRIQA es una herramienta adecuada para el control de calidad práctico en el análisis de imágenes de parásitos.
  • El modelo mejora la consistencia en los flujos de trabajo de detección y diagnóstico posteriores.
  • Este trabajo apoya una inspección de salud pública más precisa a través de una mejor evaluación de la calidad de la imagen.