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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Flame Photometry: Overview01:02

Flame Photometry: Overview

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Flame Photometry: Lab01:16

Flame Photometry: Lab

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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
360
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Reducing Line Loss01:18

Reducing Line Loss

193
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Updated: Sep 10, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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YOLO11-RLN: Un algoritmo de UAV aéreo para la detección de incendios forestales

Li Gao1, Gaohua Chen1

  • 1School of Electronic Information Engineering, Taiyuan University of Science and Technology, Shanxi Taiyuan, China.

Annals of the New York Academy of Sciences
|August 27, 2025
PubMed
Resumen

Este estudio presenta YOLO11-RLN, un algoritmo de detección de incendios forestales mejorado para drones, que aumenta significativamente la precisión y reduce las falsas alarmas en entornos complejos.

Palabras clave:
LTF (en inglés)RepVGG (en inglés)YOLO11 (en inglés)Detección de incendios forestalesFunción de pérdidael nano

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Área de la Ciencia:

  • Visión por computadora
  • Inteligencia artificial
  • Monitoreo del medio ambiente

Sus antecedentes:

  • Los modelos existentes de detección de incendios forestales luchan con la adaptabilidad, la precisión y las tasas de detección de falsos.
  • La detección basada en vehículos aéreos no tripulados (UAV) requiere algoritmos robustos para terrenos forestales complejos.

Objetivo del estudio:

  • Desarrollar un algoritmo de detección de incendios forestales orientado al UAV que supere las limitaciones actuales.
  • Mejorar la precisión de la detección, reducir los falsos positivos y mejorar la adaptabilidad de los drones para el monitoreo de incendios forestales.

Principales métodos:

  • Algoritmo propuesto YOLO11-RLN que integra el respaldo de RepVGG para la extracción de características.
  • Se introdujo un nuevo módulo de fusión de textura de línea de fuego larga (LTF) para mejorar la percepción de la característica de fuego.
  • Implementó la función de pérdida WIoU y la parametrización YOLOv8-nano para mejorar la detección de incendios pequeños y la optimización del modelo.

Principales resultados:

  • YOLO11-RLN demostró mejoras significativas con respecto a YOLO11.
  • Se logró un aumento del 7,338% en la precisión, del 5,392% en el recuerdo, del 7,862% en mAP50 y del 7,019% en mAP50-75.
  • El análisis estadístico confirmó la solidez y la importancia de las mejoras de rendimiento.

Conclusiones:

  • El algoritmo propuesto YOLO11-RLN ofrece un rendimiento superior para la detección de incendios forestales basados en UAV.
  • La integración de RepVGG, módulo LTF, pérdida WIoU y nano optimización mejora las capacidades de detección en entornos desafiantes.