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相关概念视频

Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
Conservation of Mass in Moving, Nondeforming Control Volume01:14

Conservation of Mass in Moving, Nondeforming Control Volume

Stormwater detention basins are essential in managing runoff during heavy rainfall, particularly in urban areas where impervious surfaces increase the risk of flooding. Understanding the conservation of mass in these systems allows engineers to optimize basin performance, balancing inflow, outflow, and water storage.
In the context of a detention basin, the conservation of mass states that the total mass of water entering the basin must equal the mass leaving the basin plus any accumulation of...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
Gradually Varying Flow01:29

Gradually Varying Flow

Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

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相关实验视频

Updated: May 12, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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一个完全自动化的迷你精神状态考试评估模型,使用计算机算法进行认知查.

Lihua Chen1, Meiwei Zhang2, Weihua Yu3

  • 1Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Journal of Alzheimer's disease : JAD
|February 2, 2024
PubMed
概括

一个新的自动化迷你心理状态检查 (MMSE) 工具准确地选了老年人的认知表现. 这种数字评估显示了与医生评级的高度一致性,为认知健康评估提供了一个有前途的解决方案.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.迷你精神状态考试 迷你精神状态考试进行自动化评估.认知功能 认知功能认知查是指认知查.计算机相关技术 计算机相关技术

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相关实验视频

Last Updated: May 12, 2026

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科学领域:

  • 老年学是指老年学的学科.
  • 数字健康数字健康
  • 认知科学 认知科学

背景情况:

  • 全球人口老龄化增加了对有效认知评估工具的需求.
  • 数字创新对于评估老年人的认知表现至关重要.
  • 开发自动化系统可以简化认知查过程.

研究的目的:

  • 创建一个完全自动化的迷你心理状态考试 (MMSE) 评估模型.
  • 根据医生评估,验证自动MMSE评级的一致性和准确性.

主要方法:

  • 开发了MMSE (AAM-MMSE) 的自动化评估模型,这是一个10分钟的计算机化工具,反映了传统的中国MMSE.
  • 通过将其评级与427名参与者 (平均年龄60.6) 的医生评级进行比较,评估了AAM-MMSE的有效性.

主要成果:

  • 在医生和AAM-MMSE之间发现了全尺度的高interrater可靠性 (ICC=0.952).
  • 与医生分数相比,AAM-MMSE评级的准确性为87%,偏差为1.48分.
  • 虽然与音频相关的项目显示了很高的一致性,但像"阅读和服从"这样的特定任务几乎没有公平的一致性.

结论:

  • 对于MMSE的自动评估模型 (AAM-MMSE) 提供了一个有希望的,准确的,完全自动化的认知查系统.
  • 这种数字工具可以帮助有效评估老年人群的认知功能.
  • 进一步细化可能会改善自动化评估中的特定认知任务的协议.