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相关概念视频

Boundary Layer Characteristics01:18

Boundary Layer Characteristics

67
When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
2.2K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

314
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
314
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

122
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
122
Precipitation Processes01:12

Precipitation Processes

442
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
442
Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

Design Example: Calculating Safe Diameter for Wind-Exposed Disc

55
Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
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相关实验视频

Updated: Jun 23, 2025

Generation of a Chronic Obstructive Pulmonary Disease Model in Mice by Repeated Ozone Exposure
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一个集成基于风向的动态图形网络的深度学习模型,用于臭氧预测.

Shiyi Wang1, Yiming Sun1, Haonan Gu1

  • 1College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.

The Science of the total environment
|June 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型,用于准确预测臭氧污染. 基于风向的动态时空图形网络 (WDDSTG-Net) 通过考虑实时风数据和空间关系来改善预测.

关键词:
深度学习是一种深度学习.动态图形结构 动态图形结构图表神经网络的神经网络臭氧的预测预测

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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 臭氧污染在全球范围内构成了重大环境挑战.
  • 准确的臭氧度预测对于实施有效的缓解政策至关重要.

研究的目的:

  • 开发一种新的混合深度学习模型,用于每小时预测臭氧度.
  • 通过结合动态时空数据,提高空气质量预测的准确性.

主要方法:

  • 开发了基于风向的动态时空图形网络 (WDDSTG-Net).
  • 利用基于每小时风向的动态定向图形结构来建模车站关系.
  • 采用了注意力图和序列对序列模型,用于适应性信息聚合和时间依赖提取.
  • 综合气象预报以完善臭氧预报.

主要成果:

  • 在1小时的预测中达到6.69μg/m3的平均绝对误差,在24小时的预测中达到18.63μg/m3.
  • 超过了几种经典预测模型的性能.
  • 在所有站点的综合空气质量指数 (IAQI) 预测中达到75%以上的准确性.
  • 在预测严重臭氧污染事件方面表现出强大的能力,24小时预测的真实阳性率为0.77.

结论:

  • WDDSTG-Net强调了基于数据的空气质量建模中短期风力波动和运输动态的重要性.
  • 该模型提供了一种可靠的方法来预测臭氧度和潜在的其他空气污染物.
  • 动态时空图形网络代表了先进的环境监测和预测的一个有希望的方向.