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Related Concept Videos

Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

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This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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The workability of concrete is a critical characteristic that influences the ease of mixing, handling, and finishing the concrete. It is affected by several factors including water content, aggregate properties, and admixtures like air entrainment. Water plays a fundamental role as it lubricates the concrete mix, facilitating easier movement and placement. However, the water requirement varies depending on the texture and shape of aggregates. Finer particles and angular, rough-textured...
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In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
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Laminar Flow: Problem Solving01:24

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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Related Experiment Video

Updated: Jul 15, 2025

A Quantitative Fitness Analysis Workflow
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Ten quick tips for building FAIR workflows.

Casper de Visser1, Lennart F Johansson2, Purva Kulkarni1,3,4

  • 1Medical BioSciences Department, Radboud University Medical Center, Nijmegen, the Netherlands.

Plos Computational Biology
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces 10 practical tips for applying FAIR principles for Research Software (FAIR4RS) to scientific workflows. These guidelines enhance data reproducibility and promote sustainable computational science practices.

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Area of Science:

  • Computational Science
  • Data Management
  • Open Science

Background:

  • Rapid accumulation of research data necessitates robust data management strategies.
  • Reproducible science is a global priority, challenged by increasing data volumes.
  • The FAIR principles (Findable, Accessible, Interoperable, Reusable) guide data value maximization.

Purpose of the Study:

  • To adapt the FAIR principles for research software (FAIR4RS).
  • To provide practical guidelines for applying FAIR4RS principles to scientific workflows.
  • To enhance the reproducibility and reusability of research software and workflows.

Main Methods:

  • Development of 10 quick tips for applying FAIR4RS principles to workflows.
  • Arrangement of tips according to the FAIR acronym (Findable, Accessible, Interoperable, Reusable).
  • Inclusion of explanations clarifying each tip's relevance to FAIR4RS principles.

Main Results:

  • A set of 10 actionable tips for researchers and workflow developers.
  • Clear guidance on how to implement FAIR4RS principles in computational workflows.
  • A framework to improve the sustainability and reproducibility of scientific workflows.

Conclusions:

  • The proposed tips serve as practical guidelines for workflow developers.
  • Implementation of these tips contributes to more reproducible and sustainable computational science.
  • The initiative aims to positively impact the open science and FAIR communities.