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相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

369
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
369
Stability of structures01:14

Stability of structures

157
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
157
Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K
Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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相关实验视频

Updated: Jun 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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动态堆叠组合用于跨语言代码嗅觉检测.

Hamoud Aljamaan1,2

  • 1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

PeerJ. Computer science
|September 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于在Java和Python中检测代码气味的动态组合,实现与完整堆叠组合相比的精度,但复杂性降低. 这些方法为识别软件设计问题提供了更稳定,更有效的方法.

关键词:
密码的味道是密码的味道.检测 检测 检测 检测 检测一个动态的合奏.组合学习学习 组合学习机器学习 机器学习堆叠组合组合堆叠组合组合

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 计算机科学 计算机科学

背景情况:

  • 代码气味是软件设计和实施不良的指标.
  • 对于代码嗅觉的机器学习是一个活跃的研究领域,但模型往往缺乏稳定性,主要集中在Java上.
  • 现有的代码嗅觉检测方法可能很复杂,在编程语言中可能无法很好地泛化.

研究的目的:

  • 在Java和Python中提出用于代码嗅觉的动态集合方法.
  • 调查贪搜索和落后淘汰策略在构建这些动态乐团的有效性.
  • 将动态组合的复杂性和检测性能与完整堆叠组合进行比较.

主要方法:

  • 使用贪搜索和落后淘汰策略开发动态集合模型.
  • 在四个Java和两个Python代码气味上的检测性能评估.
  • 对模型复杂度和检测准确度的比较分析,使用完整的堆叠组合.

主要成果:

  • 贪的搜索和向后消除为动态合奏产生了不同的基本模型集.
  • 动态组合的检测性能与完整堆叠组合的检测性能相当,没有显著的损失.
  • 动态组合,特别是那些使用逆向消除的组合,导致大多数研究的代码气味的模型不那么复杂.

结论:

  • 动态堆叠合集提供了一种有效和稳定的方法来检测Java和Python代码的气味.
  • 这些动态组合提供了减少复杂性的替代方案,而不是完整的堆叠组合.
  • 拟议的策略有助于在多种编程语言中改进代码嗅觉检测.