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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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相关实验视频

Updated: Jan 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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多阶段组互动和跨域融合网络,用于实时烟雾细分.

Kang Li, Feiniu Yuan, Chunmei Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |December 25, 2025
    PubMed
    概括

    本研究介绍了一种轻量级网络,用于移动设备上的实时烟雾细分. 拟议的多阶段组互动和跨域融合网络 (MGICFN) 实现了高精度与低计算复杂性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 图像处理 图像处理

    背景情况:

    • 准确的烟雾细分对于火灾预警系统至关重要,尤其是在移动平台上.
    • 现有的高精度模型通常对移动应用程序来说过于计算密集.
    • 需要轻量级,高效的烟雾细分解决方案.

    研究的目的:

    • 开发一个轻量级网络,在移动设备上实时对烟雾图像进行细分.
    • 改进烟雾特征的分析,并保护来自小型烟雾物体的信息.
    • 为了提高烟雾目标的边界检测.

    主要方法:

    • 提出了一个具有较低计算复杂性的多阶段组互动和跨域融合网络 (MGICFN).
    • 引入了一个跨域交互注意模块 (CIAM),用于合并空间和频域特征.
    • 设计了一种多阶段组互动模块 (MGIM),以防止在下采样过程中丢失信息,以及用于边界精细化的边缘增强模块 (EEM).

    主要成果:

    • 在SFS3K数据集上,MGICFN实现了88.70%的Dise和81.16%的mIoU.
    • 该模型在SYN70K测试数据集上获得了87.30%的Dice和78.68%的mIoU.
    • MGICFN模型只有0.73M的参数和0.3G的FLOP,表明高效率.

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    Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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    结论:

    • 拟议的MGICFN是用于轻量级烟雾图像分割的有效和计算效率高的解决方案.
    • 该网络在基准数据集上表现出强的性能,适合实时移动应用程序.
    • 通过改进的烟雾检测,MGICFN提供了一种有希望的方法来增强消防预警系统.