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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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Short-distance Transport of Resources02:12

Short-distance Transport of Resources

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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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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

167
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Distributed Loads01:19

Distributed Loads

626
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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Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

163
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Related Experiment Video

Updated: Sep 17, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Delivering data: A real-world dataset for last-mile delivery optimization.

Anna Vrani1, Savvas D Apostolidis1, Athanasios Ch Kapoutsis1

  • 1Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece.

Data in Brief
|June 30, 2025
PubMed
Summary

This dataset provides structured distance and time matrices for optimizing Vehicle Routing Problems (VRP) in logistics. It enables realistic modeling of delivery operations using real-world traffic data.

Keywords:
Distance matrixLogistics optimizationPharmaceutical deliveriesVRP benchmark

Related Experiment Videos

Last Updated: Sep 17, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Area of Science:

  • Operations Research
  • Logistics and Supply Chain Management
  • Data Science

Background:

  • Vehicle Routing Problems (VRP) are critical in logistics for optimizing delivery routes.
  • Real-world VRP optimization requires accurate travel time and distance data, accounting for traffic variability.
  • Existing datasets often lack comprehensive, real-world traffic-influenced travel time information.

Purpose of the Study:

  • To present a novel dataset structured for Vehicle Routing Problem (VRP) optimization.
  • To provide realistic travel time and distance matrices derived from actual logistics operations.
  • To facilitate the benchmarking and development of VRP algorithms using industry-relevant data.

Main Methods:

  • Data acquired from a Third-Party Logistics (3PL) provider's order management system.
  • Distance and time matrices generated using an API incorporating historical and real-time traffic data.
  • Four matrices created: one for distance, three for travel time (optimistic, pessimistic, most likely traffic scenarios).

Main Results:

  • A dataset comprising nine days of structured distance and time matrices for 60-85 daily delivery stops.
  • Matrices offer a complete view of travel times and distances between all locations, crucial for VRP.
  • Data reflects realistic travel conditions by considering traffic congestion variability.

Conclusions:

  • The dataset serves as a valuable benchmark for evaluating and comparing VRP algorithms.
  • Enables statistical analysis and Monte Carlo simulations for modeling routing uncertainty.
  • Supports optimization of real-world delivery operations and addresses industry logistics challenges.