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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Entropy and the Second Law of Thermodynamics01:20

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Non-customized data asset evaluation based on knowledge graph and value entropy.

Wei Zhang1, Yan Gong1, Zhinan Li1

  • 1Institute of Science and Technology Information, Beijing Academy of Science and Technology, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces a new framework for objective data asset valuation using 17 indicators and a neural network to reduce subjectivity. The model enhances data-driven asset management and pricing strategies.

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Area of Science:

  • Data Science
  • Financial Technology
  • Asset Management

Background:

  • Growing volume of non-customized data assets necessitates reliable valuation methods.
  • Existing techniques suffer from incomplete indicators and subjective judgment.
  • Need for objective and systematic data asset valuation frameworks.

Purpose of the Study:

  • To develop a structured framework for data asset valuation.
  • To enhance objectivity and accuracy in data asset assessment.
  • To provide a comprehensive evaluation view using knowledge graphs.

Main Methods:

  • A 17-indicator framework for data asset value assessment.
  • Neural networks for calculating objective indicator weights.
  • Knowledge graphs for visualizing indicator relationships.
  • Integration of information entropy and TOPSIS method for refined valuation.

Main Results:

  • Demonstrated capability in assessing Bitcoin market trends and purchasing potential.
  • Validated adaptability across diverse financial assets using BYD stock data.
  • Confirmed effectiveness in supporting data-driven asset management and pricing.

Conclusions:

  • The proposed framework offers a systematic methodology for data asset valuation.
  • The model effectively reduces subjectivity and enhances assessment accuracy.
  • Provides significant theoretical and practical implications for asset pricing.