Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pharmacokinetics Education: Addressing Core Learning Challenges Through Innovations in Teaching.

Pharmacology research & perspectives·2026
Same author

Corrigendum to Tumor microenvironment assessment-based signatures for predicting response to immunotherapy in non-small cell lung cancer [iScience Volume 29, Issue 3, March 2026, Article e114872].

iScience·2026
Same author

CRISPR/Cas12a-Enhanced Cascade Amplification for Ultra-sensitive DNA Ligase Detection.

Analytical chemistry·2026
Same author

Bayesian Mendelian randomization reveals a protective effect of later age at first sexual intercourse against erectile dysfunction.

Medicine·2026
Same author

Single-shot lensless dual-mode ultraviolet imaging based on diffuser speckle modulation.

Optics letters·2026
Same author

Five-year change in brain metabolism across the spectrum of cognitive impairment in older adults: a quantitative MRI study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Related Experiment Video

Updated: May 12, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.3K

A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening.

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
Summary

A new automated Mini-Mental State Examination (MMSE) tool accurately screens cognitive performance in older adults. This digital assessment shows high consistency with physician ratings, offering a promising solution for cognitive health evaluations.

Keywords:
Alzheimer’s diseaseMini-Mental State Examinationautomated assessmentcognitive functioncognitive screeningcomputer-related technologies

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.3K

Related Experiment Videos

Last Updated: May 12, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.3K

Area of Science:

  • Gerontology
  • Digital Health
  • Cognitive Science

Background:

  • Global population aging increases demand for effective cognitive assessment tools.
  • Digital innovations are crucial for evaluating cognitive performance in older adults.
  • Developing automated systems can streamline cognitive screening processes.

Purpose of the Study:

  • To create a fully automated Mini-Mental State Examination (MMSE) assessment model.
  • To validate the consistency and accuracy of the automated MMSE ratings against physician assessments.

Main Methods:

  • Developed the Automated Assessment Model for MMSE (AAM-MMSE), a 10-minute computerized tool mirroring the traditional Chinese MMSE.
  • Assessed AAM-MMSE validity by comparing its ratings with physician ratings in 427 participants (average age 60.6).

Main Results:

  • High interrater reliability was found between physicians and AAM-MMSE for the full scale (ICC=0.952).
  • Accuracy of AAM-MMSE ratings was 87%, with a bias of 1.48 points compared to physician scores.
  • While audio-related items showed high agreement, specific tasks like 'Reading and obey' had slight to fair agreement.

Conclusions:

  • The Automated Assessment Model for MMSE (AAM-MMSE) provides a promising, accurate, fully automated system for cognitive screening.
  • This digital tool can aid in efficiently assessing cognitive function in aging populations.
  • Further refinement may improve agreement on specific cognitive tasks within the automated assessment.