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Related Concept Videos

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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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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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Multimachine Stability01:25

Multimachine Stability

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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.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

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Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling.

K Rajammal1, M Chinnadurai2

  • 1Computer Science and Business Systems, Rajalakshmi Engineering College, Thandalam, Chennai, Tamil Nadu, 602105, India. rajeeaarthi@gmail.com.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cloud load balancing approach using Spiking Neural Networks (SNNs) and Temporal Graph Neural Networks (TGNNs) for improved efficiency and performance. The new method significantly enhances throughput, reduces response times, and lowers energy consumption in cloud environments.

Keywords:
Cloud computingLoad balancingOptimizationSpiking neural networkTemporal graph neural network

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cloud environments face significant load balancing challenges due to dynamic resource states and workloads.
  • Traditional load balancing methods struggle with real-time variations, leading to inefficient resource utilization and increased response times.

Purpose of the Study:

  • To develop a novel, adaptive load balancing approach for cloud environments.
  • To enhance resource utilization, reduce response times, and improve energy efficiency in cloud systems.

Main Methods:

  • Utilized Spiking Neural Networks (SNNs) for adaptive decision-making to identify workload fluctuations.
  • Employed Temporal Graph Neural Networks (TGNNs) for dynamic resource state modeling and future availability prediction.
  • Integrated reinforcement learning to optimize SNN decisions based on TGNN predictions.

Main Results:

  • Achieved 20% higher throughput compared to existing methods.
  • Reduced makespan by 35% and response time by 40%.
  • Lowered energy consumption by 30-40%.

Conclusions:

  • The proposed SNN-TGNN model offers significant improvements in cloud load balancing.
  • Demonstrated superior performance in throughput, energy efficiency, makespan, and response time.
  • The approach effectively manages dynamic cloud environments and workloads.