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相关概念视频

Design Consideration01:22

Design Consideration

Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key aspect...
Stress Concentrations01:13

Stress Concentrations

The concept of stress concentration is crucial for understanding how materials respond under bending stresses, particularly when there are irregularities or discontinuities in the material's geometry. Normally, stress in a symmetric member subjected to pure bending is assumed to be uniformly distributed across the entire cross-section. However, this assumption does not hold when there are variations in the cross-sectional geometry or the presence of notches and holes.
The stress concentration...
Principal Stresses: Problem Solving01:15

Principal Stresses: Problem Solving

When analyzing two planes intersecting at right angles under the influence of shearing, tensile, and compressive stresses, it is essential to identify principal planes, maximum shearing stress, and principal stresses. To find the principal planes, apply a formula that equates them to twice the shearing stress divided by the difference between tensile and compressive stresses.
Fineness Modulus01:19

Fineness Modulus

The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
Differential Leveling01:12

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
Profile Leveling and Cross Sections01:26

Profile Leveling and Cross Sections

Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the instrument's...

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Updated: Jul 11, 2026

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer
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UPFP-SG:单边外周面严重程度分级的新基准

Wei Gan, Ruiqi Zhao, Ke Lu

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |September 10, 2025
    PubMed
    概括

    开发了一种新的基准和自动化方法来分级单边外围面部的严重程度. 这种方法改善了面部神经分支功能的客观评估,优于现有的深度学习技术.

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    科学领域:

    • 医学成像和计算机视觉
    • 神经学 神经学
    • 生物医学工程 生物医学工程

    背景情况:

    • 单边面部严重影响日常生活和生活质量.
    • 目前的诊断依赖于主观的临床判断,缺乏客观的量化.
    • 有限的公共数据集和无法分析明显的面部神经分支,阻碍了自动检测.

    研究的目的:

    • 引入一个新的基准,UPFG-SG (单边外围面部的严重程度分级),用于客观评估.
    • 开发一种自动化方法,通过不同面部神经分支对面部的严重程度进行分级.
    • 解决面部研究中的主观诊断和数据稀缺性的局限性.

    主要方法:

    • 建立UPFG-SG数据集,以对外围面部神经分支进行增强的主观评估系统.
    • 开发一种新的深度学习方法,将各种面部特征整合起来,以对的严重程度进行评分.
    • 实施一个增强的回归模块,以提高严重性评估的准确性.

    主要成果:

    • 拟议的方法有效地捕捉了微妙的面部表情变化和微妙的局部细节.
    • 对UPFG-SG数据集的实验结果显示,与现有的深度学习方法相比,性能优越.
    • 开发的基准和方法为面部的分级提供了更客观,更准确的方法.

    结论:

    • UPFG-SG基准和相关的自动分级方法在评估单边外围面部方面取得了重大进展.
    • 这项工作为研究人员和临床医生提供了宝贵的资源,促进了更客观和可靠的诊断.
    • 拟议的方法有可能改善面部患者的患者护理和治疗结果.