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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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相关实验视频

Updated: Jan 23, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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使用机器学习检测 Fundus 照片中的炎症.

S Saeed Mohammadi1,2, Negin Yavari1, Aim-On Saengsirinavin1

  • 1Byers Eye Institute, Stanford University, Palo Alto, California.

Ophthalmology science
|January 22, 2026
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概括

机器学习模型可以使用超广场 fundus 照片 (UWFFPs) 来检测后段炎症,作为光素血管学 (UWFFA) 的非侵入性替代品. 这项技术显示出高精度,在某些情况下超过人类专家.

关键词:
人工智能的人工智能是人工智能.根据UWFFA的说法,美国UWFFPFP顶点AI AI 在线

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 后段炎症的诊断通常依赖于侵入性成像,如超宽场光素血管造影 (UWFFA).
  • 超广场底部摄影 (UWFFP) 提供了一种非侵入性成像替代方案.
  • 开发人工智能工具来解释UWFFPs用于炎症检测对于可访问的诊断至关重要.

研究的目的:

  • 开发和评估一个机器学习 (ML) 模型,使用UWFFPs作为UWFFA的替代品来检测后部段炎症.
  • 评估基于ML的UWFFP分析与专家评分器相比的诊断性能.

主要方法:

  • 编制了302个UWFFP的数据集,UWFFA作为炎症分类的基本真相.
  • 一个单个标签的图像分类模型使用UWFFPs的Vertex AI进行训练,以识别炎症.
  • 将ML模型的性能与UWFFPs独立组的奖学金培训专家和眼科医生的评估进行了比较.

主要成果:

  • ML模型实现了0.943的曲线下的面积,具有90.91%的灵敏度和84.21%的特异性来检测炎症.
  • 该模型在95%的额外UWFFP中正确诊断了炎症,超过了所有人类专家评分器的准确性 (85%至65%).

结论:

  • 当用ML技术分析UWFFP时,可以作为一种非侵入性和可访问的成像方式来检测后部段炎症.
  • 用人工智能对UWFFP的分析表明,对炎症检测的专家人类解释的准确性优于或相当于精确性.
  • 这种方法有望提高诊断后部部炎症状况的效率和可访问性.