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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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Fast Decoupled and DC Powerflow

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:
Distributed Loads01:19

Distributed Loads

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.
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Related Experiment Videos

Terminal-Edge-Cloud Collaborative Computation Offloading and Resource Allocation Strategy Based on Improved Mayfly

Guo-Hong Chen1, Hao-Yuan Ma1, Wang Yu1

  • 1School of Information and Electrical Engineering, Hangzhou City University, 51 Huzhou Street, Hangzhou 310015, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Improved Mayfly Algorithm (IMA) for optimizing thermal Internet of Things (TIoT) data processing in district heating systems (DHSs). The IMA significantly reduces system latency and energy consumption for smarter, low-carbon urban energy infrastructure.

Keywords:
Mixed-Integer Non-Linear Programmingdistrict heating systemsimproved mayfly algorithmresource allocationswarm intelligencetask offloadingthermal internet of things

Related Experiment Videos

Area of Science:

  • Energy Systems Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Digitalization of District Heating Systems (DHSs) generates massive real-time data via Thermal Internet of Things (TIoT) sensors.
  • Centralized computing architectures face challenges processing concurrent data and balancing low-latency control with low-energy device constraints.
  • Existing paradigms struggle to optimize the weighted sum of system latency and energy consumption.

Purpose of the Study:

  • To propose an Improved Mayfly Algorithm (IMA) for efficient data processing and task scheduling in TIoT-enabled DHSs.
  • To minimize the combined system latency and energy consumption in decentralized computing architectures.
  • To provide a low-latency, energy-efficient solution supporting the intelligent and low-carbon transformation of urban energy infrastructure.

Main Methods:

  • Development of the Improved Mayfly Algorithm (IMA) with five key enhancements: random position update masking, differential evolution (DE)-based crossover, targeted subset mutation with boundary scaling, adaptive population reset, and simulated annealing (SA)-driven local search.
  • Implementation of a collaborative computing architecture integrating TIoT sensors for DHSs.
  • Extensive simulations comparing IMA against traditional isolated computing paradigms (local-only, edge-only, cloud-only) and other metaheuristic algorithms (MA, BWO).

Main Results:

  • The proposed collaborative architecture achieved the lowest total system cost compared to isolated computing paradigms.
  • The IMA reduced the total baseline weighted cost by 17.2% compared to the standard Mayfly Algorithm (MA).
  • Under high workloads (750 concurrent tasks, 2250-dimensional MINLP space), IMA outperformed the BWO algorithm by 15.8%, demonstrating strong scalability and dominance.

Conclusions:

  • The Improved Mayfly Algorithm (IMA) offers a superior solution for scheduling tasks in TIoT-enabled DHSs, effectively minimizing latency and energy consumption.
  • The developed collaborative architecture provides a significant improvement over traditional computing paradigms for real-time data processing in smart energy systems.
  • This research supports the intelligent and low-carbon transition of urban energy infrastructure through efficient TIoT data management.