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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

627
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...
627
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

40
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Distributed Loads01:19

Distributed Loads

512
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...
512
Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

607
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.
607
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

58
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Related Experiment Video

Updated: Jun 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Dynamic clustering based risk aware congestion control technique for vehicular network.

Bhupendra Dhakad1, Sadhana Mishra2, Shailendra Singh Ojha3

  • 1ITM University, Gwalior, Madhya Pradesh, India. bhupendradhakad.ece@itmuniversity.ac.in.

Scientific Reports
|October 21, 2024
PubMed
Summary

This study introduces dynamic grouping of vehicles for safety (DGVS) to reduce network congestion in vehicular ad hoc networks (VANETs). DGVS significantly decreases traffic and communication delays, improving overall network performance for intelligent transportation systems.

Keywords:
DBSAN algorithmDynamic groupingIntelligent transport systemK mean algorithmVehicular ad-hoc network

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Vehicular Ad Hoc Networks (VANETs)
  • Network Congestion Mitigation

Background:

  • Vehicular ad hoc networks (VANETs) are crucial for road safety and advanced services within intelligent transportation systems (ITS).
  • Network congestion in VANETs poses a significant challenge to efficient data transfer and safety applications.
  • Existing methods often struggle to balance performance with congestion reduction.

Purpose of the Study:

  • To propose and evaluate a novel approach, dynamic grouping of vehicles for safety (DGVS), for mitigating network congestion in VANETs.
  • To enhance communication efficiency and reduce latency by enabling direct vehicle-to-vehicle communication within defined groups.
  • To optimize transmission rates based on channel conditions for balanced packet delivery and congestion control.

Main Methods:

  • Implementation of dynamic grouping of vehicles for safety (DGVS) using DBSCAN and K-Means clustering algorithms to create virtual regions.
  • Vehicles communicate directly within their assigned DGVS, avoiding network-wide broadcasts.
  • Assessment of DGVS efficacy through simulation-based studies, comparing performance against existing approaches.

Main Results:

  • DGVS significantly reduces network congestion in VANETs compared to traditional methods.
  • Notable improvements in overall network performance, including decreased communication delay.
  • Demonstrated ability to maintain vital data transfer for traffic control and safety applications.

Conclusions:

  • The proposed dynamic grouping of vehicles for safety (DGVS) technique is highly effective in reducing VANET congestion.
  • DGVS offers substantial gains in network performance and reduced latency.
  • Potential applications include emergency response, traffic management, and accident prevention in the transportation industry.