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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Purposive Learning01:22

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Related Experiment Video

Updated: Oct 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

704

Asynchronous Federated Learning System Based on Permissioned Blockchains.

Rong Wang1, Wei-Tek Tsai1

  • 1Digital Society & Blockchain Laboratory, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel asynchronous federated learning system using permissioned blockchains to enhance security and privacy. The new framework addresses challenges like differentiated computing power and single points of failure in traditional federated learning.

Keywords:
IoTasynchronous federated learningmulti-blockchains architecturepermissioned blockchainsprivacy protection

Related Experiment Videos

Last Updated: Oct 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

704

Area of Science:

  • Computer Science
  • Cybersecurity
  • Distributed Systems

Background:

  • Existing federated learning (FL) frameworks rely on centralized coordinators, presenting security vulnerabilities.
  • Challenges include device heterogeneity, single points of failure, privacy concerns, and lack of Byzantine fault tolerance.

Purpose of the Study:

  • To propose an asynchronous federated learning system leveraging permissioned blockchains.
  • To enhance security, privacy, and fault tolerance in federated learning environments.

Main Methods:

  • A permissioned blockchain architecture with a main-blockchain and sub-blockchains for model parameter updates.
  • An asynchronous aggregation protocol integrating learned models into the blockchain for two-order aggregation.
  • Simulation experiments to evaluate system performance.

Main Results:

  • The proposed system effectively alleviates issues in synchronous federated learning.
  • Maintains good training performance with malicious nodes and varied data quality.
  • Demonstrates robust fault tolerance.

Conclusions:

  • The asynchronous federated learning system based on permissioned blockchains offers improved security and reliability.
  • Suitable for edge computing scenarios requiring decentralized and secure model training.