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相关概念视频

Aggregates Classification01:29

Aggregates Classification

387
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...
387
Cluster Sampling Method01:20

Cluster Sampling Method

12.8K
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...
12.8K
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

240
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...
240
Types of Aggregate Grading01:15

Types of Aggregate Grading

832
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...
832
Weighted Mean00:57

Weighted Mean

5.3K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.3K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.4K
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.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
7.4K

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相关实验视频

Updated: Sep 16, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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基于塔米尔的复杂模糊的Schweizer-Sklar聚合方法,对云计算中的数据隐私技术进行排名.

Jabbar Ahmmad1, Hamiden Abd El-Wahed Khalifa2, Hafiz Muhammad Waqas1

  • 1Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan.

Scientific reports
|July 10, 2025
PubMed
概括

确保云数据隐私至关重要. 一种新的复杂模糊的Schweizer-Sklar方法有效地对数据隐私技术进行排名,解决云环境中的不确定性.

关键词:
云计算是一种云计算.决策 决策是做出决定的.优化优化 优化优化瑞士-斯克拉尔聚合运营商.塔米尔的复杂模糊集是一个复杂的模糊集.

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科学领域:

  • 计算机科学 计算机科学
  • 信息安全 信息安全
  • 人工智能的人工智能

背景情况:

  • 云计算需要对敏感信息提供强大的数据隐私解决方案.
  • 有效的数据隐私技术对于云安全和利益相关者的信任至关重要.
  • 在复杂的云环境中选择最佳隐私技术会给决策带来重大挑战.

研究的目的:

  • 引入一种新的复杂模糊的Schweizer-Sklar聚合方法来对数据隐私技术进行排名.
  • 在复杂的模糊框架内开发和分析新的聚合运算符.
  • 为云计算中优先考虑数据隐私策略提供实用方法.

主要方法:

  • 为复杂的模糊框架引入基本的瑞士-斯克拉尔运行规律.
  • 开发复杂的模糊的瑞士-斯克拉尔功率平均和几何聚合运算符.
  • 探索诸如 Idempotency,Boundedness 和单调性等属性,并开发算法.

主要成果:

  • 开发了一种用于在云环境中对数据隐私技术进行排名和优先级的新方法.
  • 该方法有效地处理隐私评估的不确定性和多维方面.
  • 一个说明性的例子和案例研究展示了实际应用和排名能力.

结论:

  • 提出的复杂模糊的Schweizer-Sklar聚合方法为云计算中的数据隐私技术选择提供了一种优越的方法.
  • 开发的理论为处理数据隐私复杂决策提供了一个强大的框架.
  • 这项工作通过提供一个系统的方式来评估和优先考虑隐私措施来增强云安全.