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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
284
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

495
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

385
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Video

Updated: Jan 14, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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An Iterative quantum chaotic security method design for big data deployments using a multi-model framework for key

Archana Kotangale1,2, Meesala Sudhir Kumar3

  • 1PhD Research Scholar Department of Computer Science and Engineering Sandip University, Nashik 422213, Maharashtra, India.

Methodsx
|October 17, 2025
PubMed
Summary

This study introduces a novel multi-layered big data security framework using chaos-driven entropy and adaptive intelligence. It enhances confidentiality, integrity, and resilience against evolving cyber threats for scalable infrastructures.

Keywords:
AnalysisAnomaly DetectionBig data securityBlockchain EncryptionDynamic key generationQuantum chaotic maps

Related Experiment Videos

Last Updated: Jan 14, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Area of Science:

  • Cybersecurity
  • Big Data Analytics
  • Information Security

Background:

  • Big data's exponential growth necessitates advanced security frameworks.
  • Existing security models struggle with high-volume, high-velocity data due to static structures and limited adaptability.
  • Traditional models lack real-time resilience assessment and dynamic key management.

Purpose of the Study:

  • To propose a novel multi-layered big data security framework.
  • To integrate chaos-driven stochastic systems (QSSS) and adaptive intelligence for enhanced security.
  • To ensure end-to-end confidentiality, integrity, and anomaly resilience in big data infrastructures.

Main Methods:

  • Development of a multi-layered security framework.
  • Integration of entropy-based key generation and adaptive encryption-compression.
  • Implementation of dynamic threat modeling, hybrid blockchain integrity, and intelligent intrusion detection.

Main Results:

  • Intrusion detection purity exceeding 95%.
  • Tampering detection rates over 96%.
  • Storage optimization achieved up to 60%.

Conclusions:

  • The proposed framework offers a next-generation solution for securing adaptable and scalable big data infrastructures.
  • The multi-layered architecture effectively combines chaos-driven entropy and adaptive intelligence.
  • The framework provides integrated protection ensuring confidentiality, integrity, and resilience in dynamic environments.