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Short-distance Transport of Resources02:12

Short-distance Transport of Resources

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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.
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Observational Learning01:12

Observational Learning

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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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Cognitive Learning01:21

Cognitive Learning

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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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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Aprendizaje con recursos limitados en redes inalámbricas

H Vincent Poor1

  • 1Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|February 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Las redes inalámbricas de próxima generación integrarán inteligencia artificial (IA) en el borde. Este artículo examina el aprendizaje federado inalámbrico, optimizando la IA para redes con recursos limitados al equilibrar energía, ancho de banda y privacidad.

Palabras clave:
aprendizaje automáticorestricciones de recursosredes inalámbricas

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Last Updated: Mar 2, 2026

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Área de la Ciencia:

  • Ciencias de la Computación
  • Ingeniería Eléctrica
  • Inteligencia Artificial

Sus antecedentes:

  • Las redes inalámbricas de próxima generación integran cada vez más la inteligencia artificial (IA) en todas las capas.
  • Una tendencia importante es la migración de funciones de IA y aprendizaje automático (ML) al borde de la red, impulsada por aplicaciones de dispositivos de borde, localidad de datos y avances en computación de niebla/borde.
  • El aprendizaje federado inalámbrico (WFL) permite la creación colaborativa de modelos en dispositivos de borde utilizando datos locales a través de un agregador.

Objetivo del estudio:

  • Explorar la integración de la inteligencia artificial y el aprendizaje automático en redes inalámbricas, centrándose específicamente en los paradigmas de computación de borde.
  • Investigar los desafíos y las compensaciones inherentes al aprendizaje federado inalámbrico (WFL) debido a la naturaleza de recursos limitados de los enlaces inalámbricos.
  • Analizar la interacción entre las características de la comunicación inalámbrica y el rendimiento de los algoritmos de ML en aplicaciones de IA de borde.

Principales métodos:

  • Exploración del aprendizaje federado inalámbrico (WFL) como marco para la IA de borde.
  • Análisis de las compensaciones entre el consumo de energía, la eficiencia del ancho de banda, la tasa de aprendizaje y la privacidad de los datos en WFL.
  • Consideración del impacto del medio inalámbrico en el diseño e implementación de aplicaciones de IA en el borde de la red.

Principales resultados:

  • El estudio destaca la necesidad de considerar las interacciones del medio inalámbrico en el diseño de IA/ML para aplicaciones de borde.
  • Existen compensaciones clave entre la eficiencia energética, el uso del ancho de banda, la velocidad de aprendizaje y la privacidad de los datos en los sistemas WFL.
  • La investigación proporciona información sobre la optimización de la implementación de IA en entornos de borde inalámbricos con recursos limitados.

Conclusiones:

  • La integración efectiva de la IA en futuras redes inalámbricas requiere un enfoque holístico, que considere tanto los aspectos de la red como los de ML.
  • La optimización del aprendizaje federado inalámbrico implica una gestión cuidadosa de las compensaciones entre las métricas de rendimiento y las restricciones de recursos.
  • Este trabajo contribuye al desarrollo de IA sostenible en el contexto de la computación de borde inalámbrico.