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相关概念视频

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

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The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Correlation of Experimental Data01:23

Correlation of Experimental Data

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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
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相关实验视频

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Surrogate Model Development for Digital Experiments in Welding
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全球大数据实验室实验,与基于内核的算法集成,并改进了用于压力强度建模的非线性组合.

Babatunde Abiodun Salami1, Jamilu Usman2, Afeez Gbadamosi3

  • 1Cardiff School of Management, Cardiff Metropolitan University, CF5 2YB, Cardiff, Wales, United Kingdom.

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PubMed
概括

这项研究预测了使用混合粘合剂的混凝土压力强度,例如磨砂颗粒高炉渣 (GGBFS) 和飞 (FA). 使用特定输入变量进行支持向量回归 (SVR),准确估计了混凝土性能,为传统水泥提供了可持续的替代方案.

关键词:
混凝土混凝土混凝土混凝土混凝土在地颗粒高炉渣.机器学习是机器学习.强大的线性回归.支持矢量回归的支持矢量回归

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 可持续的建筑材料 可持续的建筑材料
  • 计算材料科学科学 计算材料科学

背景情况:

  • 在水泥和可持续建筑实践中对减少体现碳的需求日益增长,需要探索替代粘合剂.
  • 地质颗粒高炉渣 (GGBFS) 和飞灰 (FA) 是有希望的补充性水泥材料 (SCM),可以增强混凝土的性能.
  • 准确预测混凝土的压力强度 (CS) 对结构完整性和材料优化至关重要.

研究的目的:

  • 用GGBFS,FA和普通波特兰水泥混合物来估计混凝土的压力强度.
  • 评估核心回归技术的有效性,特别是支持向量回归 (SVR),强大的线性回归 (RLR) 和多线性回归 (MLR),用于预测混凝土的压力强度.
  • 用机器学习模型确定输入变量的最佳组合,以准确预测压力强度.

主要方法:

  • 使用了3323个混凝土混合样本的数据集,有八个输入变量:水泥,FA,GGBFS,水,超塑化剂 (SP),粗聚合物 (CA),细聚合物 (Fag) 和年龄.
  • 采用线性相关性分析来评估输入特征的相对重要性.
  • 训练并评估了三个基于内核的模型 (SVR,RLR,MLR),使用三个不同的输入变量组合来预测压力强度 (CS).

主要成果:

  • 组合III,包括水泥,水,FA,SP,CA,GGBFS和F,在所有测试的回归模型中产生了最佳的预测性能.
  • 支持向量回归 (SVR) 证明了卓越的准确性,实现了0.984的R2值和0.0019的平均平方误差 (MSE) 使用组合III.
  • 开发的预测模型在研究中使用的输入变量范围内显示出高准确性.

结论:

  • 支向量回归 (SVR) 结合一组全面的输入变量 (组合III) 是非常有效和高效的预测混合混凝土的压力强度.
  • 这些发现支持使用SVR模型优化混凝土混合设计,包括SCM如GGBFS和FA.
  • 该研究强调了数据驱动方法在开发可持续建筑材料和减少对传统波特兰水泥的依赖方面的潜力.