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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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Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

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To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Related Experiment Video

Updated: Aug 8, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

Dipesh Kumar1, Nirupama Mandal1, Yugal Kumar2

  • 1Department of Electronics Engineering, Indian Institute of Technology (ISM), Dhanbad, India.

Big Data
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced shuffled frog optimization algorithm for efficient cloud task scheduling. The new method effectively reduces makespan and average cost, outperforming existing techniques.

Keywords:
cloud computingdetection algorithmfrog optimization algorithmmulti-objective modeltask scheduling

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Area of Science:

  • Cloud Computing
  • Optimization Algorithms
  • Task Scheduling

Background:

  • Exponential growth in online activities increases data load on cloud servers.
  • Efficient task scheduling is crucial for maintaining cloud application performance.
  • Existing scheduling algorithms face challenges in optimizing makespan and cost.

Purpose of the Study:

  • To propose an advanced shuffled frog optimization algorithm for cloud task scheduling.
  • To enhance the efficiency of assigning tasks to virtual machines (VMs).
  • To reduce both makespan time and average cost in cloud environments.

Main Methods:

  • Developed a novel shuffled frog optimization algorithm inspired by frog foraging behavior.
  • Introduced a new method for shuffling frog positions within a memeplex.
  • Calculated CPU cost, makespan, and a combined fitness function (budget cost + makespan).

Main Results:

  • The proposed algorithm effectively schedules tasks to virtual machines.
  • Achieved a makespan of 6, average cost of 4, and fitness of 10.
  • Demonstrated superior performance compared to W-Scheduler, SPSO-SA, and SLPSO-SA.

Conclusions:

  • The advanced shuffled frog optimization algorithm offers more effective task scheduling.
  • This method significantly reduces makespan and average cost in cloud computing.
  • The proposed technique provides a promising solution for optimizing cloud resource allocation.