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相关概念视频

Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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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 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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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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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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从真实世界的数据中使用机器学习预测复发性缺血性中风.

Noor Haidar Kadum Alsalman1,2, Amani Al-Ghraibah3, Siti Maisharah Sheikh Ghadzi4

  • 1School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia. ph.noor.alsalmany@gmail.com.

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概括

机器学习模型可以使用现实数据预测复发性缺血性中风 (RIS) 风险. 该RUSBoost模型显示出最好的表现,确定了改善患者护理的关键风险因素.

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 复发性缺血性中风 (RIS) 影响马来西亚约33%的患者.
  • 有限的研究存在于使用人工智能 (AI) 用于RIS预测与现实世界的数据.

研究的目的:

  • 开发和评估用于预测RIS的机器学习模型 (SVM,KNN,RUSBoost).
  • 为了利用来自马来西亚国家神经病学注册表的现实数据.

主要方法:

  • 在7697名患者 (2009-2016) 的回顾性研究中.
  • 开发和评估SVM,KNN和RUSBoost模型.
  • 对于不平衡的数据,应用合成少数人过量采样技术 (SMOTE).
  • 使用精度,灵敏度,特异性,PPV和AUC进行模型评估的十倍交叉验证.

主要成果:

  • RUSBoost以0.943的AUROC和86.5%的灵敏度表现出卓越的性能.
  • 在SHAP分析中,年龄,种族,血糖,高血压,高脂血症和糖尿病持续时间被确定为重要风险因素.
  • 在培训期间,SMOTE改善了RUSBoost的歧视 (AUROC=0.986).

结论:

  • 基于真实数据的机器学习是预测RIS风险的有希望的工具.
  • RUSBoost是一个可靠和可通用的模型,用于临床风险预测.
  • 将人工智能整合到临床实践中可以改善早期治疗决策和预防复发性中风的预防策略.