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Dimensional Analysis01:27

Dimensional Analysis

Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Application of Linearization and Approximation01:29

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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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The water inflow rate into a storage tank is not constant but increases over time. Initially, the pump delivers water at a rate of 5 L/min. However, the inflow rate increases by 2 L/min for each additional minute due to rising pressure or system adjustments. This scenario can be described mathematically by a linear function:It is necessary to integrate the inflow rate function to measure the total volume of water added to the tank over time. The total water volume V(t) is obtained by performing...

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Pipeline de aprendizaje profundo automatizado para la cuantificación del ángulo de callosidad

Siavash Shirzadeh Barough1, Murat Bilgel2, Catalina Ventura1

  • 1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

medRxiv : the preprint server for health sciences
|September 2, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo marco de aprendizaje profundo automatiza la medición del ángulo calloso (CA) de las imágenes de resonancia magnética, mejorando el diagnóstico de la hidrocefalia de presión normal (NPH). Este método fiable mejora la detección temprana y la evaluación clínica de la NPH.

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Área de la Ciencia:

  • Imágenes neurológicas
  • La inteligencia artificial en la medicina
  • Enfermedades neurodegenerativas

Sus antecedentes:

  • La hidrocefalia de presión normal (NPH) es una condición subdiagnosticada debido a la superposición de síntomas y al análisis manual de biomarcadores de imágenes que consume mucho tiempo.
  • Los marcadores diagnósticos clave como el ángulo calloso (AC) a menudo están sujetos a variabilidad de interpretación.

Objetivo del estudio:

  • Desarrollar un marco de aprendizaje profundo totalmente automatizado para la medición precisa del ángulo calloso (AC) a partir de exploraciones de resonancia magnética.
  • Proporcionar una alternativa robusta y reproducible a las mediciones manuales de CA para el diagnóstico de la HPN.

Principales métodos:

  • El marco integra BrainSignsNET para la detección de puntos de referencia (AC, PC) y una red basada en UNet para la segmentación lateral de los ventrículos.
  • Las imágenes de resonancia magnética se procesan previamente y se analizan utilizando una rebanada coronal perpendicular a la línea AC-PC para el cálculo de la CA.

Principales resultados:

  • El marco automatizado demostró una alta concordancia con las mediciones manuales (r = 0,98, p < 0,001) y un error medio absoluto (MAE) bajo de 2,95 grados.
  • El rendimiento fue consistente en diversos grupos demográficos de pacientes e independiente del Índice de Evans (IE).

Conclusiones:

  • El marco automatizado de medición de la CA ofrece una alternativa fiable y reproducible a los métodos manuales.
  • Esta herramienta tiene un potencial significativo para mejorar la detección y el diagnóstico tempranos de la NPH en entornos de investigación y clínicos.