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Cognitive Learning01:21

Cognitive Learning

1.2K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Cognitive Dissonance01:38

Cognitive Dissonance

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
37.5K
Reinforcement01:23

Reinforcement

934
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
934
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

17.8K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
17.8K
Reinforcements in Concrete01:25

Reinforcements in Concrete

478
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
478
Corrosion of Reinforcement01:27

Corrosion of Reinforcement

586
The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...
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Video Experimental Relacionado

Updated: Feb 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Un método de asignación de recursos del internet cognitivo de las cosas basado en un algoritmo de aprendizaje por

Rong Wang1, Yanjin Shen1, Dongtao Wang2

  • 1Hunan Automotive Engineering Vocational University, Zhuzhou, 412000, China.

Scientific reports
|February 7, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio optimiza la asignación de recursos para el Internet cognitivo de las cosas (CIoT) en vehículos para minimizar la Edad de la Información (AoI). El algoritmo mejorado de optimización de políticas proximal multiagente (IMAPPO) reduce significativamente la latencia de los datos para los vehículos conectados.

Palabras clave:
Edad de la informaciónRedes vehiculares cognitivasAprendizaje por refuerzo multiagenteAsignación de recursos

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Área de la Ciencia:

  • Comunicaciones inalámbricas
  • Internet de las cosas
  • Inteligencia artificial

Sus antecedentes:

  • La comunicación entre vehículos se enfrenta a desafíos de acceso dinámico al espectro y condiciones del canal.
  • La puntualidad es fundamental para las redes de vehículos de alta velocidad, lo que convierte a la Edad de la Información (AoI) en una métrica de rendimiento clave.

Objetivo del estudio:

  • Minimizar la Edad de la Información (AoI) en redes de Internet cognitivo de las cosas (CIoT) para vehículos.
  • Abordar la selección conjunta de canales y el control de potencia para la asignación de recursos bajo movilidad de alta velocidad.

Principales métodos:

  • Modelar el problema como un Proceso de Decisión de Markov (MDP) con una función de recompensa personalizada.
  • Emplear un enfoque de aprendizaje por refuerzo multiagente con vehículos como agentes.
  • Proponer un algoritmo mejorado de Optimización de Políticas Proximal Multiagente (IMAPPO) con redes Actor mejoradas para espacios de acción híbridos.

Principales resultados:

  • El algoritmo IMAPPO propuesto gestiona eficazmente la asignación de recursos en entornos vehiculares dinámicos.
  • Las simulaciones confirman la viabilidad y eficacia del algoritmo para reducir la AoI del sistema.
  • El esquema de asignación de recursos CIoT supera significativamente a los métodos alternativos para reducir la AoI de los usuarios de vehículos.

Conclusiones:

  • El algoritmo IMAPPO proporciona una solución eficaz para la asignación de recursos en redes vehiculares CIoT.
  • La optimización de la AoI es crucial para aplicaciones en tiempo real en vehículos conectados y autónomos.
  • Esta investigación avanza la gestión inteligente de recursos para futuros sistemas de comunicación vehicular.