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Precipitation Processes01:12

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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...
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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Binary dataset for machine learning applications to tropical cyclone formation prediction.

Chanh Kieu1, Quan Nguyen2

  • 1Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, 47405, USA. ckieu@indiana.edu.

Scientific Data
|May 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset for machine learning (ML) in tropical cyclone (TC) research. It provides customized NCEP/FNL data to aid ML model development for TC formation prediction.

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Area of Science:

  • Atmospheric science
  • Meteorology
  • Climate science

Background:

  • Machine learning (ML) applications in atmospheric science are expanding.
  • Tropical cyclone (TC) research benefits from advanced data analysis techniques.

Purpose of the Study:

  • To create a specialized dataset for developing ML models focused on tropical cyclone formation.
  • To support research on TC predictability across various forecast lead times.

Main Methods:

  • Customization of National Center for Environmental Prediction (NCEP)/Final Analysis (FNL) data.
  • Extraction of key environmental conditions relevant to TC formation.
  • Structuring data as multi-channel images with positive/negative sample directories.

Main Results:

  • A binary dataset covering 1999-2023 with environmental data for TC formation.
  • Data includes lead times from 0 to 72 hours.
  • Dataset is designed for easy integration with ML applications.

Conclusions:

  • The dataset offers a standardized resource for ML-based TC formation and predictability research.
  • Facilitates novel investigations into atmospheric conditions influencing tropical cyclones.
  • Enables exploration of TC behavior at different forecast horizons.