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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Reclosers and Fuses01:26

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Automatic circuit reclosers enhance the protection of distribution circuits by interrupting and auto-reclosing an AC circuit according to a preset sequence. They effectively manage temporary faults on overhead distribution lines, often caused by tree limbs or wildlife, by briefly disrupting service to improve overall reliability. However, contact with reclosers or energized broken conductors on the ground can pose serious hazards.
A comprehensive protection scheme for radial distribution...
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Circuit Breaker and Fuse Selection01:23

Circuit Breaker and Fuse Selection

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A circuit breaker is a device engineered to interrupt fault currents and sometimes reclose automatically. When a fault current is detected, the breaker separates the electrical contacts, which generates an arc. This arc is extinguished by methods such as elongation, cooling, or splitting, depending on the breaker's design. Breakers are categorized based on the voltage they operate at and the medium used for arc extinction, such as air, oil, SF6 gas, or vacuum.
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Classification of Elements and Compounds02:54

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Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
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High-dimensional longitudinal classification with the multinomial fused lasso.

Samrachana Adhikari1, Fabrizio Lecci2, James T Becker3

  • 1Department of Population Health, New York University School of Medicine, New York, New York.

Statistics in Medicine
|February 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing longitudinal data, identifying key dementia risk factors that change over time. The findings highlight how factors like race and vascular health become more critical in later life stages.

Keywords:
Alzheimer's diseasecardiovascular health studycognition studylongitudinal observational dataregularization

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Area of Science:

  • High-dimensional statistics
  • Longitudinal data analysis
  • Biostatistics

Background:

  • Longitudinal classification problems present unique statistical challenges.
  • Regularized estimation methods are crucial for high-dimensional data.
  • Identifying time-varying risk factors in diseases like Alzheimer's is essential.

Purpose of the Study:

  • To develop and evaluate regularized estimation techniques for high-dimensional longitudinal classification.
  • To identify adaptive change points in coefficient estimates over time.
  • To apply the method to Alzheimer's disease data and assess practical considerations.

Main Methods:

  • Utilized the lasso and fused lasso regularizers for coefficient estimation.
  • Developed an efficient algorithm based on proximal gradient descent.
  • Applied the technique to the Cardiovascular Health Study Cognition Study dataset.

Main Results:

  • Coefficient estimates demonstrated piecewise constant behavior with adaptively selected change points.
  • The method was successfully applied to Alzheimer's disease longitudinal data.
  • Risk factors such as race, gender, and cognitive decline were found to increase in relevance in later life stages.

Conclusions:

  • The proposed regularized estimation method effectively handles high-dimensional longitudinal classification.
  • The technique identifies significant time-varying predictors for diseases like Alzheimer's.
  • Practical aspects like tuning parameter selection and model stability are important for real-world application.