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Reproductive Cloning01:27

Reproductive Cloning

32.6K
Reproductive cloning is the process of producing a genetically identical copy—a clone—of an entire organism. While clones can be produced by splitting an early embryo—similar to what happens naturally with identical twins—cloning of adult animals is usually done by a process called somatic cell nuclear transfer (SCNT).
Somatic Cell Nuclear Transfer
In SCNT, an egg cell is taken from an animal and its nucleus is removed, creating an enucleated egg. Then a somatic...
32.6K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

536
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
536
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.8K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.8K
Disassembly of Intermediate Filaments01:35

Disassembly of Intermediate Filaments

2.6K
Intermediate filaments (IFs) do not undergo spontaneous disassembly. Enzymes, kinases, and phosphatases add and remove phosphates from specific sites to regulate their disassembly. The IF concentration in the cytoplasm also regulates the disassembly. If the concentration crosses a threshold, it activates the protein kinases in the vicinity, allowing the phosphorylation of IFs.
Keratin proteins, found at the cell periphery near cell junctions, undergo a cycle of assembly and disassembly. In Type...
2.6K

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相关实验视频

Updated: Jan 22, 2026

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
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A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

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使用混合中间表示和BiLSTM网络的语义代码克隆检测.

M Shahbaz Ismail1, Sara Shahzad1, Fahmi H Quradaa2

  • 1Department of Computer Science, University of Peshawar, Peshawar, Pakistan.

PloS one
|January 20, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于使用混合表示和深度双向LSTM网络检测语义代码克隆的新框架. 该方法实现了高精度,优于其他模型,并在软件维护中证明了其实际适用性.

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科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程

背景情况:

  • 语义代码克隆检测对于软件维护和质量保证至关重要.
  • 现有的方法与模糊代码扎,包括多态和变形.

研究的目的:

  • 为语义代码克隆检测提出一个强大的框架.
  • 为了应对代码模糊化技术带来的挑战.

主要方法:

  • 使用混合表示学习与深度双向长短期记忆 (BiLSTM) 网络.
  • 将模型应用于通过Soot框架提取的Baf和Jimple中间表示.
  • 整合注意力机制和多个中间表示.

主要成果:

  • 取得了高水平的培训 (98%) 和验证 (95%) 准确度,具有良好的概括性.
  • BiLSTM 模型的表现始终优于其他经常性模型.
  • 在BigCloneBench数据集上表现出最先进的性能,回忆和F1得分高达97%.

结论:

  • 混合中间表示对于语义克隆检测是有效的.
  • 拟议的BiLSTM框架为识别代码克隆提供了一个实际的解决方案,即使是在模糊的情况下.
  • 未来的研究可以探索基于变压器的模型,以提高性能.