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相关概念视频

Biasing of Metal-Semiconductor Junctions01:27

Biasing of Metal-Semiconductor Junctions

279
Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
279
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

482
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
482
Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
633
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.
On...
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马根瓦克:基于具有可变约束的生成对抗网络的金属工件减少方法.

Guang Li1, Longyin Ji1, Chenyu You2

  • 1Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China.

Physics in medicine and biology
|September 11, 2023
PubMed
概括

一种新的深度学习方法 - - 具有可变约束的金属工件减少生成对抗网络 (MARGANVAC) - - 通过减少金属工件来增强CT成像. 它克服了数据限制,并在各种CT场景中与先进技术相美.

关键词:
金属文物减少减少的方法计算机断层扫描 (CT) 是一种计算机断层扫描.生成性的对抗性网络.变量约束的变量约束

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 金属工件减少 (MAR) 在CT成像中至关重要,深度学习显示出希望.
  • 现有的深度学习 MAR 方法在图像域与端到端方法中的配对训练数据和性能限制方面面临挑战.
  • 图像域方法具有广泛的适用性,但通常缺乏性能,而高性能端到端方法由于内存限制而仅限于特定的CT类型 (风扇光束).

研究的目的:

  • 引入一种新的图像域MAR方法,MARGANVAC,以提高MAR性能.
  • 为了解决在现实世界临床环境中缺少配对训练数据的问题.
  • 开发一种适用于各种CT场景 (风扇光束和圆光束) 的方法,其性能与双域方法相提并论.

主要方法:

  • 提出了一个具有可变约束的金属工件减少生成对抗网络 (MARGANVAC),一个图像域深度学习模型.
  • 引入了一个变量约束 (时间变化的成本函数),可以在训练期间调整忠实性约束.
  • 开发了一种转移方法,通过提取真实金属痕迹并将其添加到无文物图像中来生成配对训练数据.

主要成果:

  • 在模拟风扇光束和真实圆光束CT实验中,MARGANVAC方法表现出卓越的性能.
  • 定量和定性评估证实了拟议方法相对于竞争方法的有效性.
  • 金属文物转移方法成功生成了现实的配对数据用于模型训练.

结论:

  • 马根瓦克 (MARGANVAC) 是一种多功能图像域MAR模型,适用于各种CT系统 (风扇光束,圆光束).
  • 该模型的性能与最先进的双域MAR技术相提并论.
  • 拟议的数据生成方法促进了监督MAR在实际临床场景中的实际部署.