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Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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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...
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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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...
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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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...
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Related Experiment Videos

Accurate Mediterranean Sea forecasting via graph-based deep learning.

Daniel Holmberg1,2, Emanuela Clementi3, Italo Epicoco3,4

  • 1Department of Computer Science, University of Helsinki, Helsinki, Finland. daniel.holmberg@helsinki.fi.

Scientific Reports
|December 6, 2025
PubMed
Summary
This summary is machine-generated.

SeaCast, a new neural network, offers faster and more accurate regional ocean forecasting. This machine learning model improves upon traditional methods, extending skillful predictions to 15 days for marine applications.

Keywords:
Graph neural networksLearned simulationRegional ocean forecasting

Related Experiment Videos

Area of Science:

  • Oceanography
  • Machine Learning
  • Computational Fluid Dynamics

Background:

  • Accurate ocean forecasting is vital for shipping, aquaculture, and coastal management.
  • Traditional numerical models are computationally intensive and time-consuming.
  • Machine learning offers efficient alternatives for forecasting tasks.

Purpose of the Study:

  • Introduce SeaCast, a neural network for high-resolution regional ocean forecasting.
  • Develop a machine learning approach that overcomes limitations of traditional numerical solvers.
  • Enhance the accuracy and forecast window of regional ocean prediction systems.

Main Methods:

  • Developed SeaCast, a graph-based neural network for regional ocean forecasting.
  • Integrated external forcing data relevant to regional oceanographic conditions.
  • Validated SeaCast using high-resolution experiments with the Mediterranean Sea operational forecasting system.

Main Results:

  • SeaCast consistently outperformed the operational numerical model within a 10-day forecast window.
  • SeaCast demonstrated skillful predictions extending to 15 days.
  • The graph-based framework effectively handled complex ocean grid geometries.

Conclusions:

  • SeaCast represents a significant advancement in regional ocean prediction capabilities.
  • Machine learning models like SeaCast offer a promising, efficient alternative to traditional ocean forecasting.
  • The approach has the potential to improve various marine-dependent industries through enhanced forecasting.