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Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...

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A Neuroscientific Approach to the Examination of Concussions in Student-Athletes
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在PMHS正面雪测试中评估潜水事件的方法:探索潜在指标

Karthik Somasundaram1,2, Klaus Driesslein1,2, Anjishnu Banerjee3

  • 1Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin.

Traffic injury prevention
|September 10, 2025
PubMed
概括

在死后人类受试者 (PMHS) 测试中,使用腰带负荷,骨盆旋转和干膝盖角度更好地预测潜水. 更柔软的座椅和倾斜的位置增加了潜水风险,特别是对于较小的乘客.

关键词:
潜航指标的潜航指标自动化车辆自动化车辆正面碰撞的正面碰撞.肥胖的 肥胖的 肥胖的倾斜的倾斜的倾斜小小的女性.

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科学领域:

  • 生物力学和预防伤害
  • 汽车安全研究 汽车安全研究
  • 人体建模 人体建模

背景情况:

  • 在死后人类受试者 (PMHS) 测试中评估潜水,特别是对肥胖受试者来说,存在重大挑战.
  • 了解潜水对于改善车辆安全和在正面撞击时保护乘客至关重要.

研究的目的:

  • 确定可靠的动力学和动力学指标,以评估PMHS正面雪测试中的潜水事件.
  • 根据这些指标和不同的测试条件,开发一个统计模型来预测潜水概率.

主要方法:

  • 分析了36个全身PMHS正面雪试验的数据,其中包括各种座位配置,人体特征,撞击脉冲和座椅背的角度.
  • 评估了七个响应参数,包括视觉检查,腰带负荷,骨盆旋转,阴骨骨折,干-膝盖角度,部向前移动和反潜板曲折.
  • 开发一个物流回归模型,使用引导技术和逐步的共变量选择来识别潜水的重要预测因素.

主要成果:

  • 观测者之间的高度一致 (98.5%,卡帕=0.95) 证实了潜水评估的可靠性.
  • 腰带负荷痕迹形态,骨盆旋转位移和干膝盖角度被确定为潜水最重要的预测因素.
  • 软弹座椅 (33%),斜背座椅 (21%) 和小型女性人体测量 (23%) 的潜水概率最高,而肥胖的受试者显示较低的概率 (11%).

结论:

  • 动力和动力学指标,特别是腰带负荷,骨盆旋转和干-膝盖角度,可以有效地预测PMHS测试中的潜水.
  • 诸如柔软的弹座椅,斜背座椅角度和较小的乘客尺寸等因素增加了潜水的可能性.
  • 开发的预测模型和识别的因素可以增强计算人体模型,提高汽车安全研究中伤害预测的准确性.