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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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SLPDBO-BP: an efficient valuation model for data asset value.

Cuiping Zhou1, Shaobo Li1,2, Cankun Xie1

  • 1Guizhou University, State Key Laboratory of Public Big Data, Guiyang, Guizhou, China.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the SLPDBO-BP model for data asset value assessment, improving accuracy and efficiency over traditional methods. The novel approach enhances data asset valuation by optimizing assessment processes.

Keywords:
BP neural networkData assetsDung beetle optimizerSLPDBO-BPValue assessment

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional data asset value assessment methods suffer from subjectivity and inefficiency.
  • Accurate data asset valuation is crucial for data factorization and strategic development.

Purpose of the Study:

  • To introduce a novel SLPDBO-BP model for objective and efficient data asset value assessment.
  • To enhance the global optimization capabilities for improved assessment accuracy.

Main Methods:

  • Developed the SLPDBO algorithm by integrating sinusoidal chaos mapping, Levy flight, and adaptive weight variation.
  • Evaluated SLPDBO performance using 20 test functions against existing optimization algorithms.
  • Combined SLPDBO with backpropagation (BP) to create the SLPDBO-BP model for data asset valuation.

Main Results:

  • The SLPDBO-BP model significantly improved data asset assessment accuracy.
  • Achieved reductions in mean absolute error (MAE) by 35.1%, root mean square error (RMSE) by 37.6%, and mean absolute percentage error (MAPE) by 38.7% compared to DBO-BP.
  • Demonstrated enhanced evaluation efficiency and superior simulation effects.

Conclusions:

  • The SLPDBO-BP model offers a more accurate and efficient solution for data asset value assessment.
  • The proposed optimization strategies enhance the model's ability to overcome limitations of traditional methods.
  • SLPDBO-BP provides a robust framework for reliable data asset valuation.