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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.
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The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Updated: Jul 16, 2025

Revised and Neuroimaging-Compatible Versions of the Dual Task Screen
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Corrigendum: Multi-task learning based on Stochastic Configuration Networks.

Xue-Mei Dong1, Xudong Kong1, Xiaoping Zhang1

  • 1Collaborative Innovation Center of Statistical Data Engineering, Technology and Application, School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China.

Frontiers in Bioengineering and Biotechnology
|September 20, 2023
PubMed
Summary
This summary is machine-generated.

This study corrects a previous article DOI. The corrected DOI ensures proper citation and access to the research findings.

Keywords:
knowledge sharing and transfermulti-task learningneural networksstochastic configurationsupervised mechanism

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Area of Science:

  • Biomedical Engineering
  • Scientific Publishing

Context:

  • Correction of a previously published article DOI.
  • Ensuring accurate citation and retrieval of scientific literature.

Purpose:

  • To provide the correct Digital Object Identifier (DOI) for the article.
  • To rectify an error in the original publication's metadata.

Summary:

  • The article DOI has been corrected to 10.3389/fbioe.2022.890132.
  • This correction facilitates accurate referencing and access to the research.

Impact:

  • Improved discoverability of the research article.
  • Enhanced integrity of scientific records and citation practices.