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相关概念视频

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

82
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

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Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
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Types of Aggregate Grading01:15

Types of Aggregate Grading

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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Incomplete Dominance01:43

Incomplete Dominance

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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相关实验视频

Updated: Jun 7, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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一个神经数据库,用于回答关于不完整的关系数据的聚合查询.

Sepanta Zeighami1, Raghav Seshadri1, Cyrus Shahabi1

  • 1University of Southern California, CA 90089.

IEEE transactions on knowledge and data engineering
|November 18, 2024
PubMed
概括
此摘要是机器生成的。

在不完整的数据集上,NeuroComplete直接估计了对聚合查询的答案,避免了复杂的合成数据生成. 与现有方法相比,这种新的方法显著减少了平均和计数查询的错误.

关键词:
分析查询 分析查询机器学习 机器学习缺失的数据 缺失的数据关系数据库是一个关系数据库.

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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 由于成本,隐私或集成问题,现实世界的数据集经常不完整.
  • 不完整的数据导致对汇总查询的答案不准确.
  • 现有的解决方案涉及合成数据生成,这具有挑战性,容易产生偏见.

研究的目的:

  • 通过直接估计查询答案,提出处理不完整数据集的范式转变.
  • 引入NeuroComplete,一种绕过合成数据生成的方法.
  • 为了提高对不完整的关系数据的聚合查询答案的准确性.

主要方法:

  • 从不完整的数据集中,NeuroComplete生成了带有可计算答案的查询.
  • 查询被嵌入到一个特征空间中,表示它们与相关数据部分的关系.
  • 一个神经网络是使用监督学习与查询功能和正确答案训练的神经网络.

主要成果:

  • 神经完整模型学会了对新查询的答案进行概括和准确估计.
  • 实验结果显示,对现实数据集的错误有显著的减少.
  • 与最先进的方法相比,在AVG查询中减少了4倍的错误,在COUNT查询中减少了10倍.

结论:

  • 对于不完整的数据集,NeuroComplete为合成数据生成提供了一个有效的替代方案.
  • 这种方法利用神经网络和查询嵌入来准确地回答聚合查询.
  • 该方法在减少常见聚合查询类型的错误方面表现出卓越的性能.