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相关概念视频

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview01:19

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview

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In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then...
822
Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle01:19

Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle

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Inductively coupled plasma (ICP) is the most widely used plasma source in atomic emission spectroscopy (AES), also known as Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The ICP source, or torch, consists of three concentric quartz tubes with argon gas flowing through them. A spark from a Tesla coil initiates the ionization of argon, generating a high-temperature plasma.
The ions and electrons produced interact with the fluctuating magnetic field created by a water-cooled...
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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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相关实验视频

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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一种强大的ICP初始化方法

Alexander Kolpakov, Michael Werman

    IEEE transactions on pattern analysis and machine intelligence
    |June 19, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种用于初始化代式最接近点 (ICP) 算法的新方法,增强对未标记数据的点云注册. 该方法有效匹配使用共变矩阵定义的圆体的点云,提高刚性转换的精度.

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    Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments
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    相关实验视频

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    科学领域:

    • 计算机视觉 计算机视觉
    • 计算几何学的计算几何学
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 点云注册对于3D数据分析至关重要.
    • 代最接近点 (ICP) 算法被广泛使用,但需要良好的初始化.
    • 没有标记的点云和刚性转换给ICP带来了重大挑战.

    研究的目的:

    • 为ICP算法开发一个强大的初始化方法.
    • 为了能够在刚性转换下准确地记录未标记的点云.
    • 为该方法对噪声的稳定性提供理论界限.

    主要方法:

    • 从点云共变矩阵中得出的匹配圆体.
    • 系统地测试使用有限反射组的主要半轴匹配.
    • 导出噪声强度的理论界限.

    主要成果:

    • 拟议的圆匹配方法提供了有效的ICP初始化.
    • 数值实验验证实了关于强度的理论发现.
    • 该方法在匹配未标记的点云时证明了可靠的性能.

    结论:

    • 新的初始化策略提高了未标记点云的ICP性能.
    • 该方法对噪声具有坚固性,这一点得到了理论界限和实验的证实.
    • 这项工作在3D点云注册技术方面取得了重大进展.