Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

103
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
103
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

153
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
153
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
89
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

129
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
129
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

303
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
303

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Chemical Constituents and Anti-Complement Activity of Seven <i>Rhododendron</i> Species in Tibetan Medicine.

Molecules (Basel, Switzerland)·2026
Same author

Hepatocyte TGF-β-ORP3 signaling axis promotes hepatic stellate cell activation and liver fibrogenesis in mice.

Cellular and molecular gastroenterology and hepatology·2026
Same author

Rare 19q13.42 duplication encompassing <i>PRKCG</i> associated with neurodevelopmental abnormalities.

Translational pediatrics·2026
Same author

Pathogenic germline variations and cancer risks in pediatric patients referred for genetic testing.

Nature medicine·2026
Same author

The E3 Ligase RNF8 Promotes Ubiquitination and Degradation of ChREBPα During Liver Stress Response.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

[Auricular acupoint pressing combined with visual cognitive training for children with Chinese developmental dyslexia: a randomized controlled trial].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2026

Related Experiment Video

Updated: Sep 18, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K

Optimization model for enterprise financial management utilizing genetic algorithms and fuzzy logic.

Sujuan Wang1, Musadaq Mansoor2

  • 1School of Accounting, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary

This study introduces a novel Hierarchical Reinforcement Learning with Fuzzy Reasoning (HRL-FR) model for optimizing enterprise financial management and enhancing risk prediction. The HRL-FR model significantly improves financial decision-making and accuracy.

Keywords:
BP neural networkFinancial management optimizationFuzzy logicGenetic algorithmHierarchical reinforcement learning

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K

Related Experiment Videos

Last Updated: Sep 18, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K

Area of Science:

  • Financial Management
  • Artificial Intelligence
  • Data Science

Background:

  • Enterprise financial management faces complexities in optimizing models for risk prediction.
  • Existing models often struggle with uncertainties in dynamic financial environments.

Purpose of the Study:

  • To develop and validate an advanced model for optimizing enterprise financial management.
  • To enhance risk prediction performance and decision-making capabilities.

Main Methods:

  • Developed a multi-objective mathematical model for cost reduction and capital utilization.
  • Integrated genetic algorithms with back-propagation (BP) neural networks for parameter optimization.
  • Proposed a hierarchical reinforcement learning model based on fuzzy reasoning (HRL-FR).

Main Results:

  • The HRL-FR model demonstrated superior accuracy in predicting enterprise financial management information.
  • Key financial variables like working capital asset ratio and debt-to-equity ratio were identified as significant.
  • Validated effectiveness using Compustat and CRSP datasets, showing improved profitability and efficiency.

Conclusions:

  • The HRL-FR model offers a powerful tool for optimizing financial management and mitigating risks.
  • The study highlights the potential of AI-driven approaches in complex financial environments.
  • Findings provide valuable insights for strategic decision-making in enterprises.