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Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
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Short-distance Transport of Resources02:12

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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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Mitochondrial protein import is powered by two distinct energy sources: ATP hydrolysis and electrochemical potential across the inner membrane. Newly synthesized precursors are bound by cytosolic chaperones of the Hsp70 family, which guide them to the import receptors on the mitochondrial surface. Utilizing the energy of ATP hydrolysis, Hsp70 chaperones transfer these precursors to the TOM receptors on the mitochondrial outer membrane.
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Maximum Power Flow and Line Loadability01:23

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Neuronal Communication01:28

Neuronal Communication

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Related Experiment Video

Updated: Apr 8, 2026

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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Energy-optimized 6G communication framework with intelligent resource allocation for massive IoT networks.

Mian Muhammad Kamal1, Syed Zain Ul Abideen2, Muhammad Sheraz3

  • 1School of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn.

Scientific Reports
|April 6, 2026
PubMed
Summary

This study introduces an energy-efficient framework for 6G massive Internet of Things (IoT) networks using a Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS). Joint optimization via deep reinforcement learning significantly boosts energy efficiency and network performance.

Keywords:
6G communicationDeep reinforcement learning (DRL)Energy optimizationIntelligent resource allocationLow-latency systemsMassive IoTSTAR-RISSustainable networks

Related Experiment Videos

Last Updated: Apr 8, 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

1.2K

Area of Science:

  • Wireless Communications
  • Network Optimization
  • Artificial Intelligence

Background:

  • 6G networks demand efficient resource allocation for massive Internet of Things (IoT) deployments.
  • Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) offer novel propagation control capabilities.
  • Existing methods often optimize radio resources and STAR-RIS independently, limiting overall system performance.

Purpose of the Study:

  • To propose an integrated framework for energy-optimized uplink resource allocation in 6G massive IoT networks.
  • To jointly optimize transmit power, subchannel assignment, and STAR-RIS coefficients.
  • To leverage deep reinforcement learning for efficient and scalable network management.

Main Methods:

  • Developed a novel framework integrating radio resource management and STAR-RIS control.
  • Employed a Soft Actor-Critic (SAC) agent with Gumbel-Softmax relaxation for joint optimization.
  • Utilized offline centralized training and online edge cloud coordinated execution.
  • Conducted simulations using 3GPP Urban Micro channels with up to 200 devices and a 128-element STAR-RIS.

Main Results:

  • Achieved a 24.3% increase in energy efficiency compared to baseline methods.
  • Demonstrated an 18.7% higher aggregate throughput and a 19.1% reduction in latency.
  • Extended network lifetime by 21.6% while maintaining near-optimal fairness.
  • Validated the effectiveness of deep reinforcement learning for cross-layer integration.

Conclusions:

  • Joint optimization of propagation control and radio resource allocation is crucial for 6G massive IoT.
  • Deep reinforcement learning provides a scalable and effective solution for green massive machine-type communications.
  • The proposed STAR-RIS assisted framework significantly enhances network efficiency and performance.